phd in computer science uc berkeley

Computer Science

About the Program

The Department of Electrical Engineering and Computer Sciences (EECS) offers two graduate programs in Computer Science: the Master of Science (MS), and the Doctor of Philosophy (PhD).

Master of Science (MS)

The Master of Science (MS) emphasizes research preparation and experience and, for most students, is a chance to lay the groundwork for pursuing a PhD.

Doctor of Philosophy (PhD)

The Berkeley PhD in EECS combines coursework and original research with some of the finest EECS faculty in the US, preparing for careers in academia or industry. Our alumni have gone on to hold amazing positions around the world.

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Admission to the University

Applying for graduate admission.

Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. The Graduate Division hosts a complete list of graduate academic programs, departments, degrees offered, and application deadlines can be found on the Graduate Division website.

Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application and steps to take to apply can be found on the Graduate Division website .

Admission Requirements

The minimum graduate admission requirements are:

A bachelor’s degree or recognized equivalent from an accredited institution;

A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and

Enough undergraduate training to do graduate work in your chosen field.

For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page . It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here .

Where to apply?

Visit the Berkeley Graduate Division application page .

Admission to the Program

The following items are required for admission to the Berkeley EECS MS/PhD program in addition to the University’s general graduate admissions requirements:

• Statement of Purpose: Why are you applying for this program? What will do you plan to accomplish during this degree program? What do you want to do afterward, and how will this degree help you reach that goal?
• Personal History Statement: What experiences from your past made you decide to go into this field? And how will your personal history help you succeed in this program and your future goals?
• GPA: If you attended a university outside the USA, please leave the GPA section blank.
• Resume: Please also include a full resume/CV listing your experience and education.

Complete the online UC Berkeley graduate application:

• Start your application through this link , and fill in each relevant page.
• Upload the materials above, and send the recommender links several weeks prior to the application deadline to give your recommenders time to submit their letters.

Doctoral Degree Requirements

Normative time requirements.

Normative time in the EECS department is between 5.5-6 years for the doctoral program.

Time to Advancement

The faculty of the College of Engineering recommends a minimum number of courses taken while in graduate standing. The total minimum is 24 units of coursework, taken for a letter grade and not including 297, 298, 299, 301, 375 and 602.

Preliminary Exams

The EECS preliminary requirement consists of two components.

Oral Examination

The oral exam serves an advisory role in a student's graduate studies program, giving official feedback from the exam committee of faculty members. Students must be able to demonstrate an integrated grasp of the exam area's body of knowledge in an unstructured framework. Students must pass the oral portion of the preliminary exam within their first two attempts. A third attempt is possible with a petition of support from the student's faculty adviser and final approval by the prelim committee chair. Failure to pass the oral portion of the preliminary exam will result in the student being ineligible to complete the PhD program. The examining committee awards a score in the range of 0-10. The minimum passing score is 6.0.

Breadth Courses

The breadth courses ensure that students have exposure to areas outside of their concentration. It is expected that students will achieve high academic standards in these courses.

CS students must complete courses from three of the following areas, passing each with at least a B+. One course must be selected from the Theory, AI, or Graphics/HCI group; and one course must be selected from the Programming, Systems, or Architecture/VLSI group 1 .

COMPSCI 260B ,  COMPSCI 263 , and  EL ENG 219C  cannot be used to fulfill this constraint, though they can be used to complete one of the three courses.

Qualifying Examination (QE)

The QE is an important checkpoint meant to show that a student is on a promising research track toward the PhD degree. It is a University examination, administered by the Graduate Council, with the specific purpose of demonstrating that "the student is clearly an expert in those areas of the discipline that have been specified for the examination, and that he or she can, in all likelihood, design and produce an acceptable dissertation." Despite such rigid criteria, faculty examiners recognize that the level of expertise expected is that appropriate for a third year graduate student, who may be only in the early stages of a research project.

The EECS Department offers the qualifying exam in two formats: A or B. Students may choose the exam type of their choice after consultation with their adviser.

• Students prepare a write-up and presentation, summarizing a specific research area, preferably the one in which they intend to do their dissertation work. Their summary surveys that area and describes open and interesting research problems.
• They describe why they chose these problems and indicate what direction their research may take in the future.
• They prepare to display expertise on both the topic presented and on any related material that the committee thinks is relevant.
• The student should talk (at least briefly) about any research progress they have made to date (e.g., MS project, PhD research, or class project). Some evidence of their ability to do research is expected.
• The committee shall evaluate students on the basis of their comprehension of the fundamental facts and principles that apply within their research area and students' ability to think incisively and critically about the theoretical and practical aspects of the chosen field.
•  Students must demonstrate command of the content and the ability to design and produce an acceptable dissertation.

This option includes the presentation and defense of a thesis proposal in addition to the requirements of format A. It will include a summary of research to date and plans for future work (or at least the next stage thereof). The committee shall not only evaluate the student's thesis proposal and their progress to date but shall also evaluate according to format A. As in format A, students should prepare a single document and presentation, but in this case, additional emphasis must be placed on research completed to date and plans for the remainder of the dissertation research.

Thesis Proposal Defense

Students not presenting a satisfactory thesis proposal defense, either because they took format A for the QE or because the material presented in a format B exam was not deemed a satisfactory proposal defense (although it may have sufficed to pass the QE), must write up and present a thesis proposal, which should include a summary of the student's research to date and plans for the remainder of the dissertation research. Students should be prepared to discuss background and related areas, but the focus of the proposal should be on the progress made so far, and detailed plans for completing the thesis. The standard for continuing with PhD research is that the proposal has sufficient merit to lead to a satisfactory dissertation. Another purpose of this presentation is for faculty to provide feedback on the quality of work to date. For this step, the committee should consist of at least three members from EECS familiar with the research area, preferably including those on the dissertation committee.

Normative Time in Candidacy

Advancement to candidacy.

Students must file the advancement form in the Graduate Office by no later than the end of the semester following the one in which the qualifying exam was passed. In approving this application, Graduate Division approves the dissertation committee and will send a certificate of candidacy.

Students in the EECS department are required to be advanced to candidacy at least two semesters before they are eligible to graduate.  Once a student is advanced to candidacy, candidacy is valid for five years.  For the first three years, non-resident tuition may be waived, if applicable.

Dissertation Talk

As part of the requirements for the doctoral degree, students must give a public talk on the research covered by their dissertation. The dissertation talk should be given a few months before the signing of the final submission of the dissertation. It must be given before the final submission of the dissertation.  The talk should cover all major components of the dissertation work in a substantial manner; in particular, the dissertation talk should not omit topics that will appear in the dissertation but are incomplete at the time of the talk.

The dissertation talk is to be attended by the whole dissertation committee, or, if this is not possible, by at least a majority of the members. Attendance at this talk is part of the committee's responsibility. It is, however, the responsibility of the student to schedule a time for the talk that is convenient for members of the committee. The EECS department requires that the talk be given during either the fall or spring semester.

Required Professional Development

Graduate student instructor teaching requirement.

The EECS department requires all PhD candidates to serve as Graduate Student Instructors (GSIs) within the EECS department. The GSI teaching requirement not only helps to develop a student's communication skills, but it also makes a great contribution to the department's academic community. Students must fulfill this requirement by working as a GSI (excluding  EL ENG 375 or COMPSCI 375 ) for a total of 30 hours minimum prior to graduation. At least 20 of those hours must be for an EE or CS undergraduate course. In addition, students must earn a Satisfactory grade in the mandatory pedagogy course to complete the GSI teaching requirement.

Master's Degree Requirements

Unit requirements.

A minimum of 24 units is required.

All courses must be taken for a letter grade, except for courses numbered  299, which are only offered for  S/U  credit.

Students must maintain a minimum cumulative GPA of 3.0. No credit will be given for courses in which the student earns a grade of D+ or below.

Transfer credit may be awarded for a maximum of four semester or six quarter units of graduate coursework from another institution.

For both Plan I and Plan II, MS students need to complete the departmental Advance to Candidacy form, have their research advisor sign the form, and submit the form to the Department's Master's Degree Advisor. Students who choose Plan I will also need to complete the Graduate Division's online Advancement to Candidacy form through  Calcentral  no later than the end of the second week of classes in their final semester. 

Once a student has advanced to candidacy, candidacy is valid for three years.

Capstone/Thesis (Plan I)

Students planning to use Plan I for their MS Degree will need to follow the  Graduate Division's “Thesis Filing Guidelines."  A copy of the signature page and abstract should be submitted to the Department's Master's Degree Advisor.  In addition, a copy should be uploaded to  the EECS website .

Capstone/Master's Project (Plan II)

Students planning to use Plan II for their MS Degree will need to produce an MS Plan II Title/Signature Page. A copy of the signature page and abstract should be submitted to the the Department's Master's Degree Advisor. In addition, a copy should be uploaded to  the EECS website .

There is no special formatting required for the body of the Plan II MS report, unlike the Plan I MS thesis, which must follow Graduate Division guidelines.

Select a subject to view courses

Electrical engineering and computer sciences, electrical engineering, eecs c206a introduction to robotics 4 units.

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course is an introduction to the field of robotics. It covers the fundamentals of kinematics, dynamics, control of robot manipulators, robotic vision, sensing, forward & inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics, & control. We will present techniques for geometric motion planning & obstacle avoidance. Open problems in trajectory generation with dynamic constraints will also be discussed. The course also presents the use of the same analytical techniques as manipulation for the analysis of images & computer vision. Low level vision, structure from motion, & an introduction to vision & learning will be covered. The course concludes with current applications of robotics. Introduction to Robotics: Read More [+]

Rules & Requirements

Prerequisites: Familiarity with linear algebra at level of EECS 16A / EECS 16B or MATH 54 . Experience doing coding in python at the level of COMPSCI 61A . Preferred: experience developing software at level of COMPSCI 61B and experience using Linux. EECS 120 is not required, but some knowledge of linear systems may be helpful for the control of robots

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and three hours of laboratory per week.

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructors: Sastry, Sreenath

Formerly known as: Electrical Engin and Computer Sci 206A

Also listed as: MEC ENG C206A

Introduction to Robotics: Read Less [-]

EECS C206B Robotic Manipulation and Interaction 4 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 This course is a sequel to EECS C106A /206A, which covers kinematics, dynamics and control of a single robot. This course will cover dynamics and control of groups of robotic manipulators coordinating with each other and interacting with the environment. Concepts will include an introduction to grasping and the constrained manipulation, contacts and force control for interaction with the environment. We will also cover active perception guided manipulation, as well as the manipulation of non-rigid objects. Throughout, we will emphasize design and human-robot interactions, and applications to applications in manufacturing, service robotics, tele-surgery, and locomotion. Robotic Manipulation and Interaction: Read More [+]

Prerequisites: Students are expected to have taken EECS C106A / BioE C106A / ME C106A / ME C206A/ EECS C206A or an equivalent course. A strong programming background, knowledge of Python and Matlab, and some coursework in feedback controls (such as EE C128 / ME C134) are also useful. Students who have not taken EECS C106A / BioE C106A / ME C106A / ME C206A/ EECS C206A should have a strong programming background, knowledge of Python and Matlab, and exposure to linear algebra, and Lagrangian dynamics

Instructors: Bajcsy, Sastry

Formerly known as: Electrical Engin and Computer Sci 206B

Also listed as: MEC ENG C206B

Robotic Manipulation and Interaction: Read Less [-]

EECS 208 Computational Principles for High-dimensional Data Analysis 4 Units

Terms offered: Fall 2023, Fall 2022, Fall 2021 Introduction to fundamental geometric and statistical concepts and principles of low-dimensional models for high-dimensional signal and data analysis, spanning basic theory, efficient algorithms, and diverse real-world applications. Systematic study of both sampling complexity and computational complexity for sparse, low-rank, and low-dimensional models – including important cases such as matrix completion, robust principal component analysis, dictionary learning, and deep networks. Computational Principles for High-dimensional Data Analysis: Read More [+]

Prerequisites: The following courses are recommended undergraduate linear algebra (Math 110), statistics (Stat 134), and probability (EE126). Back-ground in signal processing (ELENG 123), optimization (ELENG C227T), machine learning (CS189/289), and computer vision ( COMPSCI C280 ) may allow you to appreciate better certain aspects of the course material, but not necessary all at once. The course is open to senior undergraduates, with consent from the instructor

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructor: Ma

Computational Principles for High-dimensional Data Analysis: Read Less [-]

EECS 219A Numerical Simulation and Modeling 4 Units

Terms offered: Spring 2025, Spring 2024 Numerical simulation and modeling are enabling technologies that pervade science and engineering. This course provides a detailed introduction to the fundamental principles of these technologies and their translation to engineering practice. The course emphasizes hands-on programming in MATLAB and application to several domains, including circuits, nanotechnology, and biology. Numerical Simulation and Modeling: Read More [+]

Prerequisites: Consent of instructor; a course in linear algebra and on circuits is very useful

Credit Restrictions: Students will receive no credit for EL ENG 219A after completing EL ENG 219.

Fall and/or spring: 15 weeks - 4 hours of lecture per week

Additional Format: Four hours of lecture per week.

Instructor: Roychowdhury

Formerly known as: Electrical Engineering 219A

Numerical Simulation and Modeling: Read Less [-]

EECS 219C Formal Methods: Specification, Verification, and Synthesis 3 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 Introduction to the theory and practice of formal methods for the design and analysis of systems, with a focus on algorithmic techniques. Covers selected topics in computational logic and automata theory including modeling and specification formalisms, temporal logics, satisfiability solving, model checking, synthesis, learning, and theorem proving. Applications to software and hardware design, cyber-physical systems, robotics, computer security , and other areas will be explored as time permits. Formal Methods: Specification, Verification, and Synthesis: Read More [+]

Prerequisites: Graduate standing or consent of instructor; COMPSCI 170 is recommended

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week.

Instructor: Seshia

Formerly known as: Electrical Engineering 219C

Formal Methods: Specification, Verification, and Synthesis: Read Less [-]

EECS 225A Statistical Signal Processing 3 Units

Terms offered: Spring 2025, Spring 2023, Fall 2021 This course connects classical statistical signal processing (Hilbert space filtering theory by Wiener and Kolmogorov, state space model, signal representation, detection and estimation, adaptive filtering) with modern statistical and machine learning theory and applications. It focuses on concrete algorithms and combines principled theoretical thinking with real applications. Statistical Signal Processing: Read More [+]

Prerequisites: EL ENG 120 and EECS 126

Additional Format: Three hours of Lecture per week for 15 weeks.

Instructors: Jiao, Waller

Formerly known as: Electrical Engineering 225A

Statistical Signal Processing: Read Less [-]

EECS 225B Digital Image Processing 3 Units

Terms offered: Fall 2023, Fall 2022, Fall 2020 This course deals with computational methods as applied to digital imagery. It focuses on image sensing and acquisition, image sampling and quantization; spatial transformation, linear and nonlinear filtering; introduction to convolutional neural networks, and GANs; applications of deep learning methods to image processing problems; image enhancement, histogram equalization, image restoration, Weiner filtering, tomography, image reconstruction from projections and partial Fourier information, Radon transform, multiresolution analysis, continuous and discrete wavelet transform and computation, subband coding, image and video compression, sparse signal approximation, dictionary techniques, image and video compression standards, and more. Digital Image Processing: Read More [+]

Prerequisites: Basic knowledge of signals and systems, convolution, and Fourier Transform

Instructor: Zakhor

Formerly known as: Electrical Engineering 225B

Digital Image Processing: Read Less [-]

EECS 227AT Optimization Models in Engineering 4 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization. Optimization Models in Engineering: Read More [+]

Prerequisites: MATH 54 or consent of instructor

Credit Restrictions: Students will receive no credit for EECS 227AT after taking EECS 127 or Electrical Engineering 127/227AT.

Instructor: El Ghaoui

Formerly known as: Electrical Engineering 227AT

Optimization Models in Engineering: Read Less [-]

EECS C249B Cyber Physical System Design Prinicples and Applications 4 Units

Terms offered: Spring 2020, Spring 2019, Spring 2016 Principles of embedded system design. Focus on design methodologies and foundations. Platform-based design and communication-based design and their relationship with design time, re-use, and performance. Models of computation and their use in design capture, manipulation, verification, and synthesis. Mapping into architecture and systems platforms. Performance estimation. Scheduling and real-time requirements. Synchronous languages and time-triggered protocols to simplify the design process. Cyber Physical System Design Prinicples and Applications: Read More [+]

Prerequisites: Suggested but not required: CS170, EECS149/249A

Credit Restrictions: Students will receive no credit for EECS C249B after completing EL ENG 249, or EECS 249B. A deficient grade in EECS C249B may be removed by taking EECS 249B.

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 2 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and two hours of laboratory per week.

Instructor: Sangiovanni-Vincentelli

Formerly known as: Electrical Engineering C249B/Civil and Environmental Engineering C289

Also listed as: CIV ENG C289

Cyber Physical System Design Prinicples and Applications: Read Less [-]

EECS 251A Introduction to Digital Design and Integrated Circuits 3 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 An introduction to digital circuit and system design. The material provides a top-down view of the principles, components, and methodologies for large scale digital system design. The underlying CMOS devices and manufacturing technologies are introduced, but quickly abstracted to higher levels to focus the class on design of larger digital modules for both FPGAs (field programmable gate arrays) and ASICs (application specific integrated circuits). The class includes extensive use of industrial grade design automation and verification tools for assignments, labs, and projects. Introduction to Digital Design and Integrated Circuits: Read More [+]

Objectives & Outcomes

Course Objectives: The Verilog hardware description language is introduced and used. Basic digital system design concepts, Boolean operations/combinational logic, sequential elements and finite-state-machines, are described. Design of larger building blocks such as arithmetic units, interconnection networks, input/output units, as well as memory design (SRAM, Caches, FIFOs) and integration are also covered. Parallelism, pipelining and other micro-architectural optimizations are introduced. A number of physical design issues visible at the architecture level are covered as well, such as interconnects, power, and reliability.

Student Learning Outcomes: Although the syllabus is the same as EECS151, the assignments and exams for EECS251A will have harder problems that test deeper understanding expected from a graduate level course.

Prerequisites: EECS 16A and EECS 16B ; COMPSCI 61C ; and recommended: EL ENG 105 . Students must enroll concurrently in at least one the laboratory flavors EECS 251LA or EECS 251LB . Students wishing to take a second laboratory flavor next term can sign-up only for that laboratory section and receive a letter grade. The prerequisite for “Lab-only” enrollment that term will be EECS 251A from previous terms

Credit Restrictions: Students must enroll concurrently in at least one the laboratory flavors Electrical Engineering and Computer Science 251LA or Electrical Engineering and Computer Science 251LB. Students wishing to take a second laboratory flavor next term can sign-up only for that laboratory section and receive a letter grade. The pre-requisite for “Lab-only” enrollment that term will be Electrical Engineering and Computer Science 251A from previous terms.

Instructors: Stojanovic, Wawrzynek

Formerly known as: Electrical Engineering 241A

Introduction to Digital Design and Integrated Circuits: Read Less [-]

EECS 251B Advanced Digital Integrated Circuits and Systems 4 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 This course aims to convey a knowledge of advanced concepts of digital circuit and system-on-a-chip design in state-of-the-art technologies. Emphasis is on the circuit and system design and optimization for both energy efficiency and high performance for use in a broad range of applications, from edge computing to datacenters. Special attention will be devoted to the most important challenges facing digital circuit designers in the coming decade. The course is accompanied with practical laboratory exercises that introduce students to modern tool flows. Advanced Digital Integrated Circuits and Systems: Read More [+]

Prerequisites: Introduction to Digital Design and Integrated Circuits, EECS151 (taken with either EECS151LA or EECS151LB lab) or EECS251A (taken with either EECS251LA or EECS251LB lab)

Credit Restrictions: Students will receive no credit for EECS 251B after completing COMPSCI 250 , or EL ENG 241B .

Fall and/or spring: 15 weeks - 4 hours of lecture and 1 hour of discussion per week

Additional Format: Four hours of lecture and one hour of discussion per week.

Instructors: Nikolić, Shao, Wawrzynek, Asanović, Stojanović, Seshia

Advanced Digital Integrated Circuits and Systems: Read Less [-]

EECS 251LA Introduction to Digital Design and Integrated Circuits Lab 2 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 This lab lays the foundation of modern digital design by first presenting the scripting and hardware description language base for specification of digital systems and interactions with tool flows. The labs are centered on a large design with the focus on rapid design space exploration. The lab exercises culminate with a project design, e.g. implementation of a 3-stage RISC-V processor with a register file and caches. The design is mapped to simulation and layout specification. Introduction to Digital Design and Integrated Circuits Lab: Read More [+]

Course Objectives: Software testing of digital designs is covered leading to a set of exercises that cover the design flow. Digital synthesis, floor-planning, placement and routing are covered, as well as tools to evaluate timing and power consumption. Chip-level assembly is covered, including instantiation of custom blocks: I/O pads, memories, PLLs, etc.

Student Learning Outcomes: Although the syllabus is the same as EECS151LA, the assignments and exams for EECS251LA will have harder problems in labs and in the project that test deeper understanding expected from a graduate level course.

Prerequisites: EECS 16A , EECS 16B , and COMPSCI 61C ; and EL ENG 105 is recommended

Fall and/or spring: 15 weeks - 3 hours of laboratory per week

Additional Format: Three hours of laboratory per week.

Introduction to Digital Design and Integrated Circuits Lab: Read Less [-]

EECS 251LB Introduction to Digital Design and Integrated Circuits Lab 2 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 This lab covers the design of modern digital systems with Field-Programmable Gate Array (FPGA) platforms. A series of lab exercises provide the background and practice of digital design using a modern FPGA design tool flow. Digital synthesis, partitioning, placement, routing, and simulation tools for FPGAs are covered in detail. The labs exercises culminate with a large design project, e.g., an implementation of a full 3-stage RISC-V processor system, with caches, graphics acceleration, and external peripheral components. The design is mapped and demonstrated on an FPGA hardware platform. Introduction to Digital Design and Integrated Circuits Lab: Read More [+]

Student Learning Outcomes: Although the syllabus is the same as EECS151LB, the assignments and exams for EECS251LB will have harder problems in labs and in the project that test deeper understanding expected from a graduate level course.

COMPSCI C200A Principles and Techniques of Data Science 4 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024, Spring 2023, Spring 2022, Spring 2021, Spring 2020 Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project. Principles and Techniques of Data Science: Read More [+]

Prerequisites: COMPSCI C8 / INFO C8 / STAT C8 or ENGIN 7 ; and either COMPSCI 61A or COMPSCI 88. Corequisites: MATH 54 or EECS 16A

Credit Restrictions: Students will receive no credit for DATA C200 \ COMPSCI C200A \ STAT C200C after completing DATA C100 .

Fall and/or spring: 8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week 15 weeks - 3-3 hours of lecture, 1-1 hours of discussion, and 0-1 hours of laboratory per week

Summer: 8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and zero to one hours of laboratory per week. Six hours of lecture and two hours of discussion and zero to two hours of laboratory per week for 8 weeks. Six hours of lecture and two hours of discussion and zero to two hours of laboratory per week for 8 weeks.

Subject/Course Level: Computer Science/Graduate

Formerly known as: Statistics C200C/Computer Science C200A

Also listed as: DATA C200/STAT C200C

Principles and Techniques of Data Science: Read Less [-]

COMPSCI C249A Introduction to Embedded Systems 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course introduces students to the basics of models, analysis tools, and control for embedded systems operating in real time. Students learn how to combine physical processes with computation. Topics include models of computation, control, analysis and verification, interfacing with the physical world, mapping to platforms, and distributed embedded systems. The course has a strong laboratory component, with emphasis on a semester-long sequence of projects. Introduction to Embedded Systems: Read More [+]

Credit Restrictions: Students will receive no credit for Electrical Engineering/Computer Science C249A after completing Electrical Engineering/Computer Science C149.

Fall and/or spring: 15 weeks - 3 hours of lecture and 3 hours of laboratory per week

Additional Format: Three hours of lecture and three hours of laboratory per week.

Instructors: Lee, Seshia

Formerly known as: Electrical Engineering C249M/Computer Science C249M

Also listed as: EL ENG C249A

Introduction to Embedded Systems: Read Less [-]

COMPSCI 250 VLSI Systems Design 4 Units

Terms offered: Fall 2020, Spring 2017, Spring 2016 Unified top-down and bottom-up design of integrated circuits and systems concentrating on architectural and topological issues. VLSI architectures, systolic arrays, self-timed systems. Trends in VLSI development. Physical limits. Tradeoffs in custom-design, standard cells, gate arrays. VLSI design tools. VLSI Systems Design: Read More [+]

Prerequisites: COMPSCI 150

Fall and/or spring: 15 weeks - 3 hours of lecture and 4 hours of laboratory per week

Additional Format: Three hours of lecture and four hours of laboratory per week.

Instructor: Wawrzynek

VLSI Systems Design: Read Less [-]

COMPSCI 252A Graduate Computer Architecture 4 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 Graduate survey of contemporary computer organizations covering: early systems, CPU design, instruction sets, control, processors, busses, ALU, memory, I/O interfaces, connection networks, virtual memory, pipelined computers, multiprocessors, and case studies. Term paper or project is required. Graduate Computer Architecture: Read More [+]

Prerequisites: COMPSCI 61C

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week

Additional Format: Three hours of lecture and two hours of discussion per week.

Instructors: Asanović, Kubiatowicz

Formerly known as: Computer Science 252

Graduate Computer Architecture: Read Less [-]

COMPSCI 260A User Interface Design and Development 4 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 The design, implementation, and evaluation of user interfaces. User-centered design and task analysis. Conceptual models and interface metaphors. Usability inspection and evaluation methods. Analysis of user study data. Input methods (keyboard, pointing, touch, tangible) and input models. Visual design principles. Interface prototyping and implementation methodologies and tools. Students will develop a user interface for a specific task and target user group in teams. User Interface Design and Development: Read More [+]

Prerequisites: COMPSCI 61B , COMPSCI 61BL , or consent of instructor

Credit Restrictions: Students will receive no credit for Computer Science 260A after taking Computer Science 160.

Instructors: Agrawala, Canny, Hartmann

User Interface Design and Development: Read Less [-]

COMPSCI 260B Human-Computer Interaction Research 3 Units

Terms offered: Fall 2024, Fall 2017 This course is a broad introduction to conducting research in Human-Computer Interaction. Students will become familiar with seminal and recent literature; learn to review and critique research papers; re-implement and evaluate important existing systems; and gain experience in conducting research. Topics include input devices, computer-supported cooperative work, crowdsourcing, design tools, evaluation methods, search and mobile interfaces, usable security , help and tutorial systems. Human-Computer Interaction Research: Read More [+]

Prerequisites: COMPSCI 160 recommended, or consent of instructor

Instructor: Hartmann

Human-Computer Interaction Research: Read Less [-]

COMPSCI 261 Security in Computer Systems 3 Units

Terms offered: Fall 2023, Spring 2021, Fall 2018 Graduate survey of modern topics in computer security, including protection, access control, distributed access security, firewalls, secure coding practices, safe languages, mobile code, and case studies from real-world systems. May also cover cryptographic protocols, privacy and anonymity, and/or other topics as time permits. Security in Computer Systems: Read More [+]

Prerequisites: COMPSCI 162

Instructors: D. Song, Wagner

Security in Computer Systems: Read Less [-]

COMPSCI 261N Internet and Network Security 4 Units

Terms offered: Spring 2020, Fall 2016, Spring 2015 Develops a thorough grounding in Internet and network security suitable for those interested in conducting research in the area or those more broadly interested in security or networking. Potential topics include denial-of-service; capabilities; network intrusion detection/prevention; worms; forensics; scanning; traffic analysis; legal issues; web attacks; anonymity; wireless and networked devices; honeypots; botnets; scams; underground economy; attacker infrastructure; research pitfalls. Internet and Network Security: Read More [+]

Prerequisites: EL ENG 122 or equivalent; and COMPSCI 161 or familiarity with basic security concepts

Instructor: Paxson

Internet and Network Security: Read Less [-]

COMPSCI 262A Advanced Topics in Computer Systems 4 Units

Terms offered: Spring 2025, Fall 2023, Fall 2022 Graduate survey of systems for managing computation and information, covering a breadth of topics: early systems; volatile memory management, including virtual memory and buffer management; persistent memory systems, including both file systems and transactional storage managers; storage metadata, physical vs. logical naming, schemas, process scheduling, threading and concurrency control; system support for networking, including remote procedure calls, transactional RPC, TCP, and active messages; security infrastructure; extensible systems and APIs; performance analysis and engineering of large software systems. Homework assignments, exam, and term paper or project required. Advanced Topics in Computer Systems: Read More [+]

Prerequisites: COMPSCI 162 and entrance exam

Instructors: Brewer, Hellerstein

Formerly known as: 262

Advanced Topics in Computer Systems: Read Less [-]

COMPSCI 262B Advanced Topics in Computer Systems 3 Units

Terms offered: Spring 2020, Spring 2009, Fall 2008 Continued graduate survey of large-scale systems for managing information and computation. Topics include basic performance measurement; extensibility, with attention to protection, security, and management of abstract data types; index structures, including support for concurrency and recovery; parallelism, including parallel architectures, query processing and scheduling; distributed data management, including distributed and mobile file systems and databases; distributed caching; large-scale data analysis and search. Homework assignments, exam, and term paper or project required. Advanced Topics in Computer Systems: Read More [+]

Prerequisites: COMPSCI 262A

Instructors: Brewer, Culler, Hellerstein, Joseph

COMPSCI 263 Design of Programming Languages 3 Units

Terms offered: Fall 2021, Fall 2019, Spring 2019 Selected topics from: analysis, comparison, and design of programming languages, formal description of syntax and semantics, advanced programming techniques, structured programming, debugging, verification of programs and compilers, and proofs of correctness. Design of Programming Languages: Read More [+]

Prerequisites: COMPSCI 164

Instructor: Necula

Design of Programming Languages: Read Less [-]

COMPSCI 264 Implementation of Programming Languages 4 Units

Terms offered: Spring 2025, Fall 2023, Fall 2021 Compiler construction. Lexical analysis, syntax analysis. Semantic analysis code generation and optimization. Storage management. Run-time organization. Implementation of Programming Languages: Read More [+]

Prerequisites: COMPSCI 164 ; COMPSCI 263 recommended

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 6 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and six hours of laboratory per week.

Instructor: Bodik

Implementation of Programming Languages: Read Less [-]

COMPSCI 265 Compiler Optimization and Code Generation 3 Units

Terms offered: Fall 2024, Fall 2009, Spring 2003 Table-driven and retargetable code generators. Register management. Flow analysis and global optimization methods. Code optimization for advanced languages and architectures. Local code improvement. Optimization by program transformation. Selected additional topics. A term paper or project is required. Compiler Optimization and Code Generation: Read More [+]

Instructor: Sen

Compiler Optimization and Code Generation: Read Less [-]

COMPSCI C267 Applications of Parallel Computers 3 - 4 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023, Spring 2022, Spring 2021 Models for parallel programming. Overview of parallelism in scientific applications and study of parallel algorithms for linear algebra, particles, meshes, sorting, FFT, graphs, machine learning, etc. Survey of parallel machines and machine structures. Programming shared- and distributed-memory parallel computers, GPUs, and cloud platforms. Parallel programming languages, compilers, libraries and toolboxes. Data partitioning techniques. Techniques for synchronization and load balancing. Detailed study and algorithm/program development of medium sized applications. Applications of Parallel Computers: Read More [+]

Prerequisites: No formal pre-requisites. Prior programming experience with a low-level language such as C, C++, or Fortran is recommended but not required. CS C267 is intended to be useful for students from many departments and with different backgrounds, although we will assume reasonable programming skills in a conventional (non-parallel) language, as well as enough mathematical skills to understand the problems and algorithmic solutions presented

Repeat rules: Course may be repeated for credit without restriction.

Fall and/or spring: 15 weeks - 3-3 hours of lecture and 1-1 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of laboratory per week.

Instructors: Demmel, Yelick

Also listed as: ENGIN C233

Applications of Parallel Computers: Read Less [-]

COMPSCI W267 Applications of Parallel Computers 3 Units

Terms offered: Prior to 2007 Parallel programming, from laptops to supercomputers to the cloud. Goals include writing programs that run fast while minimizing programming effort. Parallel architectures and programming languages and models, including shared memory (eg OpenMP on your multicore laptop), distributed memory (MPI and UPC on a supercomputer), GPUs (CUDA and OpenCL), and cloud (MapReduce, Hadoop and Spark). Parallel algorithms and software tools for common computations (eg dense and sparse linear algebra, graphs, structured grids). Tools for load balancing, performance analysis, debugging. How high level applications are built (eg climate modeling). On-line lectures and office hours. Applications of Parallel Computers: Read More [+]

Student Learning Outcomes: An understanding of computer architectures at a high level, in order to understand what can and cannot be done in parallel, and the relative costs of operations like arithmetic, moving data, etc. To master parallel programming languages and models for different computer architectures To recognize programming "patterns" to use the best available algorithms and software to implement them. To understand sources of parallelism and locality in simulation in designing fast algorithms

Prerequisites: Computer Science W266 or the consent of the instructor

Credit Restrictions: Students will receive no credit for Computer Science W267 after completing Computer Science C267.

Fall and/or spring: 15 weeks - 3 hours of web-based lecture per week

Additional Format: Three hours of web-based lecture per week.

Online: This is an online course.

COMPSCI 268 Computer Networks 3 Units

Terms offered: Spring 2023, Spring 2021, Spring 2019 Distributed systems, their notivations, applications, and organization. The network component. Network architectures. Local and long-haul networks, technologies, and topologies. Data link, network, and transport protocols. Point-to-point and broadcast networks. Routing and congestion control. Higher-level protocols. Naming. Internetworking. Examples and case studies. Computer Networks: Read More [+]

Instructors: Joseph, Katz, Stoica

Formerly known as: 292V

Computer Networks: Read Less [-]

COMPSCI 270 Combinatorial Algorithms and Data Structures 3 Units

Terms offered: Fall 2024, Spring 2023, Spring 2021 Design and analysis of efficient algorithms for combinatorial problems. Network flow theory, matching theory, matroid theory; augmenting-path algorithms; branch-and-bound algorithms; data structure techniques for efficient implementation of combinatorial algorithms; analysis of data structures; applications of data structure techniques to sorting, searching, and geometric problems. Combinatorial Algorithms and Data Structures: Read More [+]

Prerequisites: COMPSCI 170

Instructors: Papadimitriou, Rao, Sinclair, Vazirani

Combinatorial Algorithms and Data Structures: Read Less [-]

COMPSCI 271 Randomness and Computation 3 Units

Terms offered: Fall 2024, Fall 2022, Spring 2020 Computational applications of randomness and computational theories of randomness. Approximate counting and uniform generation of combinatorial objects, rapid convergence of random walks on expander graphs, explicit construction of expander graphs, randomized reductions, Kolmogorov complexity, pseudo-random number generation, semi-random sources. Randomness and Computation: Read More [+]

Prerequisites: COMPSCI 170 and at least one course from the following: COMPSCI 270 - COMPSCI 279

Instructor: Sinclair

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COMPSCI 272 Foundations of Decisions, Learning, and Games 4 Units

Terms offered: Not yet offered This course introduces students to the mathematical foundation of learning in the presence of strategic and societal agency. This is a theory-oriented course that will draw from the statistical and computational foundations of machine learning, computer science, and economics. As a research-oriented course, a range of advanced topics will be explored to paint a comprehensive picture of classical and modern approaches to learning for the purpose of decision making.These topics include foundations of learning, foundations of algorithmic game theory, cooperative and non-cooperative games, equilibria and dynamics, learning in games, information asymmetries, mechanism design, and learning with incentives. Foundations of Decisions, Learning, and Games: Read More [+]

Prerequisites: Graduate-level mathematical maturity, including proof-based graduate-level courses in at least two, but recommended three, of the following categories: Statistics and Probability, e.g., STAT205A, STAT210B Economics, e.g., ECON207A Algorithms, e.g., CS270 Optimization, e.g., EE 227B Control theory, e.g., EE 221A

Credit Restrictions: Students will receive no credit for COMPSCI 272 after completing COMPSCI 272 . A deficient grade in COMPSCI 272 may be removed by taking COMPSCI 272 .

Instructors: Jordan, Haghtalab

Foundations of Decisions, Learning, and Games: Read Less [-]

COMPSCI 276 Cryptography 3 Units

Terms offered: Fall 2024, Fall 2020, Fall 2018 Graduate survey of modern topics on theory, foundations, and applications of modern cryptography. One-way functions; pseudorandomness; encryption; authentication; public-key cryptosystems; notions of security. May also cover zero-knowledge proofs, multi-party cryptographic protocols, practical applications, and/or other topics, as time permits. Cryptography: Read More [+]

Instructors: Trevisan, Wagner

Cryptography: Read Less [-]

COMPSCI 278 Machine-Based Complexity Theory 3 Units

Terms offered: Spring 2024, Spring 2021, Fall 2016 Properties of abstract complexity measures; Determinism vs. nondeterminism; time vs. space; complexity hierarchies; aspects of the P-NP question; relative power of various abstract machines. Machine-Based Complexity Theory: Read More [+]

Prerequisites: 170

Instructor: Trevisan

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COMPSCI 280A Intro to Computer Vision and Computational Photography 4 Units

Terms offered: Fall 2024, Fall 2023 This course introduces students to computing with visual data (images and video). We will cover acquisition, representation, and manipulation of visual information from digital photographs (image processing), image analysis and visual understanding (computer vision), and image synthesis (computational photography). Key algorithms will be presented, ranging from classical to contemporary, with an emphasis on using these techniques to build practical systems. The hands-on emphasis will be reflected in the programming assignments, where students will acquire their own images and develop, largely from scratch, image analysis and synthesis tools for real-world applications. Intro to Computer Vision and Computational Photography: Read More [+]

Course Objectives: Students will learn classic algorithms in image manipulation with Gaussian and Laplacian Pyramids, understand the hierarchy of image transformations including homographies, and how to warp an image with these transformations., Students will learn how to apply Convolutional Neural Networks for computer vision problems and how they can be used for image manipulation. Students will learn the fundamentals of 3D vision: stereo, multi-view geometry, camera calibration, structure-frommotion, multi-view stereo, and the plenoptic function mechanics of a pin-hole camera, representation of images as pixels, physics of light and the process of image formation, to manipulating the visual information using signal processing techniques in the spatial and frequency domains.

Student Learning Outcomes: After this class, students will be comfortable implementing, from scratch, these algorithms in modern programming languages and deep learning libraries.

Prerequisites: COMPSCI 61B ; MATH 53 ; and MATH 54 , MATH 56 , MATH 110 , or EECS 16A . COMPSCI C182 or COMPSCI 189 should be taken as a co-requisite

Instructors: Efros, Kanazawa

Intro to Computer Vision and Computational Photography: Read Less [-]

COMPSCI C280 Computer Vision 3 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 Paradigms for computational vision. Relation to human visual perception. Mathematical techniques for representing and reasoning, with curves, surfaces and volumes. Illumination and reflectance models. Color perception. Image segmentation and aggregation. Methods for bottom-up three dimensional shape recovery: Line drawing analysis, stereo, shading, motion, texture. Use of object models for prediction and recognition. Computer Vision: Read More [+]

Prerequisites: MATH 1A; MATH 1B; MATH 53 ; and MATH 54 (Knowledge of linear algebra and calculus)

Instructor: Malik

Also listed as: VIS SCI C280

Computer Vision: Read Less [-]

COMPSCI C281A Statistical Learning Theory 3 Units

Terms offered: Fall 2023, Fall 2021, Fall 2020 Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods. Statistical Learning Theory: Read More [+]

Instructors: Bartlett, Jordan, Wainwright

Also listed as: STAT C241A

Statistical Learning Theory: Read Less [-]

COMPSCI C281B Advanced Topics in Learning and Decision Making 3 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning. Advanced Topics in Learning and Decision Making: Read More [+]

Also listed as: STAT C241B

Advanced Topics in Learning and Decision Making: Read Less [-]

COMPSCI 282A Designing, Visualizing and Understanding Deep Neural Networks 4 Units

Terms offered: Spring 2025, Fall 2023, Spring 2023 Deep Networks have revolutionized computer vision, language technology, robotics and control. They have growing impact in many other areas of science and engineering. They do not however, follow a closed or compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground. Designing, Visualizing and Understanding Deep Neural Networks: Read More [+]

Student Learning Outcomes: Students will come to understand visualizing deep networks. Exploring the training and use of deep networks with visualization tools. Students will learn design principles and best practices: design motifs that work well in particular domains, structure optimization and parameter optimization. Understanding deep networks. Methods with formal guarantees: generative and adversarial models, tensor factorization.

Prerequisites: MATH 53 and MATH 54 or equivalent; COMPSCI 70 or STAT 134 ; COMPSCI 61B or equivalent; COMPSCI 189 or COMPSCI 289A (recommended)

Instructor: Canny

Designing, Visualizing and Understanding Deep Neural Networks: Read Less [-]

COMPSCI 284A Foundations of Computer Graphics 4 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 Techniques of modeling objects for the purpose of computer rendering: boundary representations, constructive solids geometry, hierarchical scene descriptions. Mathematical techniques for curve and surface representation. Basic elements of a computer graphics rendering pipeline; architecture of modern graphics display devices. Geometrical transformations such as rotation, scaling, translation, and their matrix representations. Homogeneous coordinates, projective and perspective transformations. Foundations of Computer Graphics: Read More [+]

Prerequisites: COMPSCI 61B or COMPSCI 61BL ; programming skills in C, C++, or Java; linear algebra and calculus; or consent of instructor

Credit Restrictions: Students will receive no credit for Computer Science 284A after taking 184.

Instructors: Agrawala, Barsky, O'Brien, Ramamoorthi, Sequin

Foundations of Computer Graphics: Read Less [-]

COMPSCI 284B Advanced Computer Graphics Algorithms and Techniques 4 Units

Terms offered: Spring 2024, Spring 2022, Spring 2019 This course provides a graduate-level introduction to advanced computer graphics algorithms and techniques. Students should already be familiar with basic concepts such as transformations, scan-conversion, scene graphs, shading, and light transport. Topics covered in this course include global illumination, mesh processing, subdivision surfaces, basic differential geometry, physically based animation, inverse kinematics, imaging and computational photography, and precomputed light transport. Advanced Computer Graphics Algorithms and Techniques: Read More [+]

Prerequisites: COMPSCI 184

Instructors: O'Brien, Ramamoorthi

Formerly known as: Computer Science 283

Advanced Computer Graphics Algorithms and Techniques: Read Less [-]

COMPSCI 285 Deep Reinforcement Learning, Decision Making, and Control 3 Units

Terms offered: Fall 2023, Fall 2022, Fall 2021 Intersection of control, reinforcement learning, and deep learning. Deep learning methods, which train large parametric function approximators, achieve excellent results on problems that require reasoning about unstructured real-world situations (e.g., computer vision, speech recognition, NLP). Advanced treatment of the reinforcement learning formalism, the most critical model-free reinforcement learning algorithms (policy gradients, value function and Q-function learning, and actor-critic), a discussion of model-based reinforcement learning algorithms, an overview of imitation learning, and a range of advanced topics (e.g., exploration, model-based learning with video prediction, transfer learning, multi-task learning, and meta-learning). Deep Reinforcement Learning, Decision Making, and Control: Read More [+]

Student Learning Outcomes: Provide an opportunity to embark on a research-level final project with support from course staff. Provide hands-on experience with several commonly used RL algorithms; Provide students with an overview of advanced deep reinforcement learning topics, including current research trends; Provide students with foundational knowledge to understand deep reinforcement learning algorithms;

Prerequisites: CS189/289A or equivalent is a prerequisite for the course. This course will assume some familiarity with reinforcement learning, numerical optimization and machine learning, as well as a basic working knowledge of how to train deep neural networks (which is taught in CS182 and briefly covered in CS189)

Instructors: Levine, Abbeel

Deep Reinforcement Learning, Decision Making, and Control: Read Less [-]

COMPSCI 286 Implementation of Data Base Systems 3 Units

Terms offered: Fall 2009, Spring 2009, Spring 2008 Implementation of data base systems on modern hardware systems. Considerations concerning operating system design, including buffering, page size, prefetching, etc. Query processing algorithms, design of crash recovery and concurrency control systems. Implementation of distributed data bases and data base machines. Implementation of Data Base Systems: Read More [+]

Prerequisites: COMPSCI 162 and COMPSCI 186 ; or COMPSCI 286A

Instructors: Franklin, Hellerstein

Formerly known as: Computer Science 286B

Implementation of Data Base Systems: Read Less [-]

COMPSCI 286A Introduction to Database Systems 4 Units

Terms offered: Spring 2018, Fall 2017, Spring 2017 Access methods and file systems to facilitate data access. Hierarchical, network, relational, and object-oriented data models. Query languages for models. Embedding query languages in programming languages. Database services including protection, integrity control, and alternative views of data. High-level interfaces including application generators, browsers, and report writers. Introduction to transaction processing. Database system implementation to be done as term project. Introduction to Database Systems: Read More [+]

Prerequisites: COMPSCI 61B and COMPSCI 61C

Credit Restrictions: Students will receive no credit for CS 286A after taking CS 186.

Introduction to Database Systems: Read Less [-]

COMPSCI 287 Advanced Robotics 3 Units

Terms offered: Fall 2019, Fall 2015, Spring 2015 Advanced topics related to current research in algorithms and artificial intelligence for robotics. Planning, control, and estimation for realistic robot systems, taking into account: dynamic constraints, control and sensing uncertainty, and non-holonomic motion constraints. Advanced Robotics: Read More [+]

Prerequisites: Instructor consent for undergraduate and masters students

Instructor: Abbeel

Advanced Robotics: Read Less [-]

COMPSCI 287H Algorithmic Human-Robot Interaction 4 Units

Terms offered: Spring 2023, Spring 2021, Spring 2020 As robot autonomy advances, it becomes more and more important to develop algorithms that are not solely functional, but also mindful of the end-user. How should the robot move differently when it's moving in the presence of a human? How should it learn from user feedback? How should it assist the user in accomplishing day to day tasks? These are the questions we will investigate in this course. We will contrast existing algorithms in robotics with studies in human-robot interaction, discussing how to tackle interaction challenges in an algorithmic way, with the goal of enabling generalization across robots and tasks. We will also sharpen research skills: giving good talks, experimental design, statistical analysis, literature surveys. Algorithmic Human-Robot Interaction: Read More [+]

Student Learning Outcomes: Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to apply Bayesian inference and learning techniques to enhance coordination in collaborative tasks. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to apply optimization techniques to generate motion for HRI. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to contrast and relate model-based and model-free learning from demonstration. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to develop a basic understanding of verbal and non-verbal communication. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to ground algorithmic HRI in the relvant psychology background. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to tease out the intricacies of developing algorithms that support HRI. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to analyze and diagram the literature related to a particular topic. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to communicate scientific content to a peer audience. Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to critique a scientific paper's experimental design and analysis.

Instructor: Dragan

Algorithmic Human-Robot Interaction: Read Less [-]

COMPSCI 288 Natural Language Processing 4 Units

Terms offered: Fall 2024, Fall 2023, Spring 2023 Methods and models for the analysis of natural (human) language data. Topics include: language modeling, speech recognition, linguistic analysis (syntactic parsing, semantic analysis, reference resolution, discourse modeling), machine translation, information extraction, question answering, and computational linguistics techniques. Natural Language Processing: Read More [+]

Prerequisites: COMPSCI 188 ; and COMPSCI 170 is recommended

Instructor: Klein

Natural Language Processing: Read Less [-]

COMPSCI 289A Introduction to Machine Learning 4 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 This course provides an introduction to theoretical foundations, algorithms, and methodologies for machine learning, emphasizing the role of probability and optimization and exploring a variety of real-world applications. Students are expected to have a solid foundation in calculus and linear algebra as well as exposure to the basic tools of logic and probability, and should be familiar with at least one modern, high-level programming lang uage. Introduction to Machine Learning: Read More [+]

Prerequisites: MATH 53 , MATH 54 , COMPSCI 70 , and COMPSCI 188 ; or consent of instructor

Credit Restrictions: Students will receive no credit for Comp Sci 289A after taking Comp Sci 189.

Instructors: Listgarten, Malik, Recht, Sahai, Shewchuk

Introduction to Machine Learning: Read Less [-]

COMPSCI 294 Special Topics 1 - 4 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 Topics will vary from semester to semester. See Computer Science Division announcements. Special Topics: Read More [+]

Fall and/or spring: 4 weeks - 3-15 hours of lecture per week 6 weeks - 3-9 hours of lecture per week 8 weeks - 2-6 hours of lecture per week 10 weeks - 2-5 hours of lecture per week 15 weeks - 1-3 hours of lecture per week

Additional Format: One to three hours of lecture per week for standard offering. In some instances, condensed special topics classes running from 2-10 weeks may also be offered usually to accommodate guest instructors. Total works hours will remain the same but more work in a given week will be required.

Special Topics: Read Less [-]

COMPSCI 297 Field Studies in Computer Science 0 - 12 Units

Terms offered: Fall 2022, Spring 2016, Fall 2015 Supervised experience in off-campus companies relevant to specific aspects and applications of electrical engineering and/or computer science. Written report required at the end of the semester. Field Studies in Computer Science: Read More [+]

Fall and/or spring: 15 weeks - 1-12 hours of independent study per week

Summer: 6 weeks - 1-30 hours of independent study per week 8 weeks - 1.5-22.5 hours of independent study per week 10 weeks - 1-18 hours of independent study per week

Additional Format: Independent study. Independent study.

Grading: Offered for satisfactory/unsatisfactory grade only.

Field Studies in Computer Science: Read Less [-]

COMPSCI 298 Group Studies Seminars, or Group Research 1 - 4 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 Advanced study in various subjects through seminars on topics to be selected each year, informal group studies of special problems, group participation in comprehensive design problems, or group research on complete problems for analysis and experimentation. Group Studies Seminars, or Group Research: Read More [+]

Repeat rules: Course may be repeated for credit without restriction. Students may enroll in multiple sections of this course within the same semester.

Fall and/or spring: 15 weeks - 1-4 hours of lecture per week

Additional Format: One to four hours of lecture per week.

Grading: The grading option will be decided by the instructor when the class is offered.

Group Studies Seminars, or Group Research: Read Less [-]

COMPSCI 299 Individual Research 1 - 12 Units

Terms offered: Fall 2023, Fall 2022, Summer 2017 Second 6 Week Session Investigations of problems in computer science. Individual Research: Read More [+]

Fall and/or spring: 15 weeks - 0-1 hours of independent study per week

Summer: 6 weeks - 8-30 hours of independent study per week 8 weeks - 6-22.5 hours of independent study per week 10 weeks - 1.5-18 hours of independent study per week

Additional Format: Independent study. Forty-five hours of work per unit per term.

Individual Research: Read Less [-]

COMPSCI 302 Designing Computer Science Education 3 Units

Terms offered: Spring 2025, Spring 2023, Spring 2022 Discussion and review of research and practice relating to the teaching of computer science: knowledge organization and misconceptions, curriculum and topic organization, evaluation, collaborative learning, technology use, and administrative issues. As part of a semester-long project to design a computer science course, participants invent and refine a variety of homework and exam activities, and evaluate alternatives for textbooks, grading and other administrative policies, and innovative uses of technology. Designing Computer Science Education: Read More [+]

Prerequisites: COMPSCI 301 and two semesters of GSI experience

Fall and/or spring: 15 weeks - 2 hours of lecture per week

Additional Format: Two hours of lecture per week.

Subject/Course Level: Computer Science/Professional course for teachers or prospective teachers

Instructor: Garcia

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COMPSCI 365 Introduction to Instructional Methods in Computer Science for Academic Interns 2 - 4 Units

Terms offered: Spring 2025, Fall 2024 This is a course for aspiring Academic Interns (AIs). It provides pedagogical training and guidance to students by introducing them to the Big Ideas of Teaching and Learning, and how to put them into practice. The course covers what makes a safe learning environment, how students learn, how to guide students toward mastery, and psychosocial factors that can negatively affect even the best students and best teachers. Class covers both theoretical and practical pedagogical aspects of teaching STEM subjects—specifically Computer Science. An integral feature of the course lies in the weekly AI experience that students perform to practice their teaching skills. Introduction to Instructional Methods in Computer Science for Academic Interns: Read More [+]

Prerequisites: Completion of any DS or CS lower-division course and concurrent participation in the Academic Intern experience in EECS at UC Berkeley

Fall and/or spring: 15 weeks - 2-2 hours of lecture and 3-9 hours of fieldwork per week

Summer: 8 weeks - 4-4 hours of lecture and 6-18 hours of fieldwork per week

Additional Format: Two hours of lecture and three to nine hours of fieldwork per week. Four hours of lecture and six to eightteen hours of fieldwork per week for 8 weeks.

Instructors: Hunn, Garcia

Introduction to Instructional Methods in Computer Science for Academic Interns: Read Less [-]

COMPSCI 370 Adaptive Instruction Methods in Computer Science 3 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 This is a course for aspiring teachers or those who want to instruct with expertise from evidence-based research and proven equity-oriented practices. It provides pedagogical training by introducing the big ideas of teaching and learning, and illustrating how to put them into practice. The course is divided into three sections—instructing the individual; a group; and psycho-social factors that affect learning at any level. These sections are designed to enhance any intern’s, tutor’s, or TA’s teaching skillset. Class is discussion based, and covers theoretical and practical pedagogical aspects to teaching in STEM. An integral feature of the course involves providing weekly tutoring sessions. Adaptive Instruction Methods in Computer Science: Read More [+]

Prerequisites: Prerequisite satisfied Concurrently: experience tutoring or as an academic intern; or concurrently serving as an academic intern while taking course

Instructor: Hunn

Adaptive Instruction Methods in Computer Science: Read Less [-]

COMPSCI 375 Teaching Techniques for Computer Science 2 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 Discussion and practice of techniques for effective teaching, focusing on issues most relevant to teaching assistants in computer science courses. Teaching Techniques for Computer Science: Read More [+]

Prerequisites: Consent of instructor

Fall and/or spring: 15 weeks - 2 hours of discussion per week

Summer: 8 weeks - 4 hours of discussion per week

Additional Format: Two hours of discussion per week. Four hours of discussion per week for 8 weeks.

Instructors: Barsky, Garcia, Harvey

Teaching Techniques for Computer Science: Read Less [-]

COMPSCI 399 Professional Preparation: Supervised Teaching of Computer Science 1 or 2 Units

Terms offered: Spring 2020, Fall 2018, Fall 2016 Discussion, problem review and development, guidance of computer science laboratory sections, course development, supervised practice teaching. Professional Preparation: Supervised Teaching of Computer Science: Read More [+]

Prerequisites: Appointment as graduate student instructor

Fall and/or spring: 15 weeks - 1-2 hours of independent study per week

Summer: 8 weeks - 1-2 hours of independent study per week

Additional Format: One hour of meeting with instructor plus 10 hours (1 unit) or 20 hours(2 units) of teaching per week. One hour of meeting with instructor plus 20 hours (1 unit) or 40 hours (2 units) of teaching per week.

Professional Preparation: Supervised Teaching of Computer Science: Read Less [-]

COMPSCI 602 Individual Study for Doctoral Students 1 - 8 Units

Terms offered: Fall 2015, Fall 2014, Spring 2014 Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. (and other doctoral degrees). Individual Study for Doctoral Students: Read More [+]

Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree.

Fall and/or spring: 15 weeks - 0 hours of independent study per week

Summer: 8 weeks - 6-45 hours of independent study per week

Additional Format: Forty-five hours of work per unit per term. Independent study, consultation with faculty member.

Subject/Course Level: Computer Science/Graduate examination preparation

Individual Study for Doctoral Students: Read Less [-]

EL ENG 206A Introduction to Robotics 4 Units

Terms offered: Fall 2017, Fall 2016, Fall 2015 An introduction to the kinematics, dynamics, and control of robot manipulators, robotic vision, and sensing. The course will cover forward and inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics and control-position, and force control. Proximity, tactile, and force sensing. Network modeling, stability, and fidelity in teleoperation and medical applications of robotics. Introduction to Robotics: Read More [+]

Prerequisites: 120 or equivalent, or consent of instructor

Credit Restrictions: Students will receive no credit for 206A after taking C125/Bioengineering C125 or EE C106A

Additional Format: Three hours of Lecture, One hour of Discussion, and Three hours of Laboratory per week for 15 weeks.

Subject/Course Level: Electrical Engineering/Graduate

Instructor: Bajcsy

Formerly known as: Electrical Engineering 215A

EL ENG 206B Robotic Manipulation and Interaction 4 Units

Terms offered: Spring 2018, Spring 2017 This course is a sequel to EECS 125/225, which covers kinematics, dynamics and control of a single robot. This course will cover dynamics and control of groups of robotic manipulators coordinating with each other and interacting with the environment. Concepts will include an introduction to grasping and the constrained manipulation, contacts and force control for interaction with the environment. We will also cover active perception guided manipulation, as well as the manipulation of non-rigid objects. Throughout, we will emphasize design and human-robot interactions, and applications to applications in manufacturing, service robotics, tele-surgery, and locomotion. Robotic Manipulation and Interaction: Read More [+]

Course Objectives: To teach students the connection between the geometry, physics of manipulators with experimental setups that include sensors, control of large degrees of freedom manipulators, mobile robots and different grippers.

Student Learning Outcomes: By the end of the course students will be able to build a complete system composed of perceptual planning and autonomously controlled manipulators and /or mobile systems, justified by predictive theoretical models of performance.

Prerequisites: EL ENG 206A / BIO ENG C125 ; or consent of the instructor

Additional Format: Three hours of lecture and three hours of laboratory and one hour of discussion per week.

EL ENG 210 Applied Electromagnetic Theory 3 Units

Terms offered: Spring 2011, Spring 2010, Fall 2006 Advanced treatment of classical electromagnetic theory with engineering applications. Boundary value problems in electrostatics. Applications of Maxwell's Equations to the study of waveguides, resonant cavities, optical fiber guides, Gaussian optics, diffraction, scattering, and antennas. Applied Electromagnetic Theory: Read More [+]

Prerequisites: EL ENG 117 ; or PHYSICS 110A and PHYSICS 110B

Formerly known as: 210A-210B

Applied Electromagnetic Theory: Read Less [-]

EL ENG 213A Power Electronics 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Power conversion circuits and techniques. Characterization and design of magnetic devices including transformers, inductors, and electromagnetic actuators. Characteristics of power semiconductor devices, including power diodes, SCRs, MOSFETs, IGBTs, and emerging wide bandgap devices. Applications to renewable energy systems, high-efficiency lighting, power management in mobile electronics, and electric machine drives. Simulation based laboratory and design project. Power Electronics: Read More [+]

Prerequisites: EL ENG 105 or background in circuit analysis (KVL, KCL, voltage/current relationships, etc.)

Instructors: Pilawa, Boles

Power Electronics: Read Less [-]

EL ENG 213B Power Electronics Design 4 Units

Terms offered: Spring 2025, Spring 2024 This course is the second in a two-semester series to equip students with the skills needed to analyze, design, and prototype power electronic converters. While EE 113/213A provides an overview of power electronics fundamentals and applications, EE 113B/213B focuses on the practical design and hardware implementation of power converters. The primary focus of EE 113B/213B is time in the laboratory, with sequential modules on topics such as power electronic components, PCB layout, closed-loop control, and experimental validation. At the end of the course, students will have designed, prototyped, and validated a power converter from scratch, demonstrating a skill set that is critical for power electronics engineers in research and industry. Power Electronics Design: Read More [+]

Repeat rules: Course may be repeated for credit with instructor consent.

Fall and/or spring: 15 weeks - 1.5 hours of lecture and 6 hours of laboratory per week

Additional Format: One and one-half hours of lecture and six hours of laboratory per week.

Instructor: Boles

Power Electronics Design: Read Less [-]

EL ENG C213 X-rays and Extreme Ultraviolet Radiation 3 Units

Terms offered: Spring 2025, Spring 2022, Spring 2021, Fall 2019 This course explores modern developments in the physics and applications of x-rays and extreme ultraviolet (EUV) radiation. It begins with a review of electromagnetic radiation at short wavelengths including dipole radiation, scattering and refractive index, using a semi-classical atomic model. Subject matter includes the generation of x-rays with synchrotron radiation, high harmonic generation, x-ray free electron lasers, laser-plasma sources. Spatial and temporal coherence concepts are explained. Optics appropriate for this spectral region are described. Applications include nanoscale and astrophysical imaging, femtosecond and attosecond probing of electron dynamics in molecules and solids, EUV lithography, and materials characteristics. X-rays and Extreme Ultraviolet Radiation: Read More [+]

Prerequisites: Physics 110, 137, and Mathematics 53, 54 or equivalent

Instructor: Attwood

Also listed as: AST C210

X-rays and Extreme Ultraviolet Radiation: Read Less [-]

EL ENG 218A Introduction to Optical Engineering 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Fundamental principles of optical systems. Geometrical optics and aberration theory. Stops and apertures, prisms, and mirrors. Diffraction and interference. Optical materials and coatings. Radiometry and photometry. Basic optical devices and the human eye. The design of optical systems. Lasers, fiber optics, and holography. Introduction to Optical Engineering: Read More [+]

Prerequisites: MATH 53 ; EECS 16A and EECS 16B , or MATH 54

Credit Restrictions: Students will receive no credit for Electrical Engineering 218A after taking Electrical Engineering 118 or 119.

Instructors: Waller, Kante

Introduction to Optical Engineering: Read Less [-]

EL ENG 219B Logic Synthesis 4 Units

Terms offered: Spring 2016, Spring 2015, Spring 2011 The course covers the fundamental techniques for the design and analysis of digital circuits. The goal is to provide a detailed understanding of basic logic synthesis and analysis algorithms, and to enable students to apply this knowledge in the design of digital systems and EDA tools. The course will present combinational circuit optimization (two-level and multi-level synthesis), sequential circuit optimization (state encoding, retiming) , timing analysis, testing, and logic verification. Logic Synthesis: Read More [+]

Additional Format: Three hours of Lecture and One hour of Discussion per week for 15 weeks.

Logic Synthesis: Read Less [-]

EL ENG C220A Advanced Control Systems I 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Input-output and state space representation of linear continuous and discrete time dynamic systems. Controllability, observability, and stability. Modeling and identification. Design and analysis of single and multi-variable feedback control systems in transform and time domain. State observer. Feedforward/preview control. Application to engineering systems. Advanced Control Systems I: Read More [+]

Instructors: Borrelli, Horowitz, Tomizuka, Tomlin

Also listed as: MEC ENG C232

Advanced Control Systems I: Read Less [-]

EL ENG C220B Experiential Advanced Control Design I 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Experience-based learning in the design of SISO and MIMO feedback controllers for linear systems. The student will master skills needed to apply linear control design and analysis tools to classical and modern control problems. In particular, the participant will be exposed to and develop expertise in two key control design technologies: frequency-domain control synthesis and time-domain optimization-based approach. Experiential Advanced Control Design I: Read More [+]

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

Additional Format: Three hours of Lecture and Two hours of Laboratory per week for 15 weeks.

Also listed as: MEC ENG C231A

Experiential Advanced Control Design I: Read Less [-]

EL ENG C220C Experiential Advanced Control Design II 3 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 Experience-based learning in design, analysis, & verification of automatic control for uncertain systems. The course emphasizes use of practical algorithms, including thorough computer implementation for representative problems. The student will master skills needed to apply advanced model-based control analysis, design, and estimation to a variety of industrial applications. First-principles analysis is provided to explain and support the algorithms & methods. The course emphasizes model-based state estimation, including the Kalman filter, and particle filter. Optimal feedback control of uncertain systems is also discussed (the linear quadratic Gaussian problem) as well as considerations of transforming continuous-time to discrete time. Experiential Advanced Control Design II: Read More [+]

Prerequisites: Undergraduate controls course (e.g. MECENG 132, ELENG 128) Recommended: MECENG C231A/ELENG C220B and either MECENG C232/ELENG C220A or ELENG 221A

Instructor: Mueller

Also listed as: MEC ENG C231B

Experiential Advanced Control Design II: Read Less [-]

EL ENG C220D Input/Output Methods for Compositional System Analysis 2 Units

Terms offered: Prior to 2007 Introduction to input/output concepts from control theory, systems as operators in signal spaces, passivity and small-gain theorems, dissipativity theory, integral quadratic constraints. Compositional stabilility and performance certification for interconnected systems from subsystems input/output properties. Case studies in multi-agent systems, biological networks, Internet congestion control, and adaptive control. Input/Output Methods for Compositional System Analysis: Read More [+]

Course Objectives: Standard computational tools for control synthesis and verification do not scale well to large-scale, networked systems in emerging applications. This course presents a compositional methodology suitable when the subsystems are amenable to analytical and computational methods but the interconnection, taken as a whole, is beyond the reach of these methods. The main idea is to break up the task of certifying desired stability and performance properties into subproblems of manageable size using input/output properties. Students learn about the fundamental theory, as well as relevant algorithms and applications in several domains.

Instructors: Arcak, Packard

Also listed as: MEC ENG C220D

Input/Output Methods for Compositional System Analysis: Read Less [-]

EL ENG 221A Linear System Theory 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Basic system concepts; state-space and I/O representation. Properties of linear systems. Controllability, observability, minimality, state and output-feedback. Stability. Observers. Characteristic polynomial. Nyquist test. Linear System Theory: Read More [+]

Prerequisites: EL ENG 120 ; and MATH 110 recommended

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of recitation per week

Additional Format: Three hours of Lecture and Two hours of Recitation per week for 15 weeks.

Linear System Theory: Read Less [-]

EL ENG 222 Nonlinear Systems--Analysis, Stability and Control 3 Units

Terms offered: Spring 2017, Spring 2016, Spring 2015 Basic graduate course in non-linear systems. Second Order systems. Numerical solution methods, the describing function method, linearization. Stability - direct and indirect methods of Lyapunov. Applications to the Lure problem - Popov, circle criterion. Input-Output stability. Additional topics include: bifurcations of dynamical systems, introduction to the "geometric" theory of control for nonlinear systems, passivity concepts and dissipative dynamical systems. Nonlinear Systems--Analysis, Stability and Control: Read More [+]

Prerequisites: EL ENG 221A (may be taken concurrently)

Nonlinear Systems--Analysis, Stability and Control: Read Less [-]

EL ENG C222 Nonlinear Systems 3 Units

Terms offered: Spring 2025, Spring 2023, Spring 2022, Spring 2021 Basic graduate course in nonlinear systems. Nonlinear phenomena, planar systems, bifurcations, center manifolds, existence and uniqueness theorems. Lyapunov’s direct and indirect methods, Lyapunov-based feedback stabilization. Input-to-state and input-output stability, and dissipativity theory. Computation techniques for nonlinear system analysis and design. Feedback linearization and sliding mode control methods. Nonlinear Systems: Read More [+]

Prerequisites: MATH 54 (undergraduate level ordinary differential equations and linear algebra)

Instructors: Arcak, Tomlin, Kameshwar

Also listed as: MEC ENG C237

Nonlinear Systems: Read Less [-]

EL ENG 223 Stochastic Systems: Estimation and Control 3 Units

Terms offered: Spring 2025, Spring 2024, Fall 2022 Parameter and state estimation. System identification. Nonlinear filtering. Stochastic control. Adaptive control. Stochastic Systems: Estimation and Control: Read More [+]

Prerequisites: EL ENG 226A (which students are encouraged to take concurrently)

Stochastic Systems: Estimation and Control: Read Less [-]

EL ENG 224A Digital Communications 4 Units

Terms offered: Fall 2010, Fall 2009, Fall 2008 Introduction to the basic principles of the design and analysis of modern digital communication systems. Topics include source coding; channel coding; baseband and passband modulation techniques; receiver design; channel equalization; information theoretic techniques; block, convolutional, and trellis coding techniques; multiuser communications and spread spectrum; multi-carrier techniques and FDM; carrier and symbol synchronization. Applications to design of digital telephone modems, compact disks, and digital wireless communication systems are illustrated. The concepts are illustrated by a sequence of MATLAB exercises. Digital Communications: Read More [+]

Prerequisites: EL ENG 120 and EL ENG 126

Additional Format: Four hours of Lecture and One hour of Discussion per week for 15 weeks.

Formerly known as: 224

Digital Communications: Read Less [-]

EL ENG 224B Fundamentals of Wireless Communication 3 Units

Terms offered: Spring 2013, Spring 2012, Spring 2010 Introduction of the fundamentals of wireless communication. Modeling of the wireless multipath fading channel and its basic physical parameters. Coherent and noncoherent reception. Diversity techniques over time, frequency, and space. Spread spectrum communication. Multiple access and interference management in wireless networks. Frequency re-use, sectorization. Multiple access techniques: TDMA, CDMA, OFDM. Capacity of wireless channels. Opportunistic communication. Multiple antenna systems: spatial multiplexing, space-time codes. Examples from existing wireless standards. Fundamentals of Wireless Communication: Read More [+]

Prerequisites: EL ENG 121 and EL ENG 226A

Instructor: Tse

Fundamentals of Wireless Communication: Read Less [-]

EL ENG 225D Audio Signal Processing in Humans and Machines 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Introduction to relevant signal processing and basics of pattern recognition. Introduction to coding, synthesis, and recognition. Models of speech and music production and perception. Signal processing for speech analysis. Pitch perception and auditory spectral analysis with applications to speech and music. Vocoders and music synthesizers. Statistical speech recognition, including introduction to Hidden Markov Model and Neural Network approac hes. Audio Signal Processing in Humans and Machines: Read More [+]

Prerequisites: EL ENG 123 and STAT 200A ; or graduate standing and consent of instructor

Instructor: Morgan

Audio Signal Processing in Humans and Machines: Read Less [-]

EL ENG C225E Principles of Magnetic Resonance Imaging 4 Units

Terms offered: Spring 2025, Spring 2023, Spring 2021 Fundamentals of MRI including signal-to-noise ratio, resolution, and contrast as dictated by physics, pulse sequences, and instrumentation. Image reconstruction via 2D FFT methods. Fast imaging reconstruction via convolution-back projection and gridding methods and FFTs. Hardware for modern MRI scanners including main field, gradient fields, RF coils, and shim supplies. Software for MRI including imaging methods such as 2D FT, RARE, SSFP , spiral and echo planar imaging methods. Principles of Magnetic Resonance Imaging: Read More [+]

Course Objectives: Graduate level understanding of physics, hardware, and systems engineering description of image formation, and image reconstruction in MRI. Experience in Imaging with different MR Imaging systems. This course should enable students to begin graduate level research at Berkeley (Neuroscience labs, EECS and Bioengineering), LBNL or at UCSF (Radiology and Bioengineering) at an advanced level and make research-level contribution

Prerequisites: EL ENG 120 or BIO ENG C165 / EL ENG C145B or consent of instructor

Credit Restrictions: Students will receive no credit for Bioengineering C265/El Engineering C225E after taking El Engineering 265.

Repeat rules: Course may be repeated for credit under special circumstances: Students can only receive credit for 1 of the 2 versions of the class,BioEc265 or EE c225e, not both

Instructors: Conolly, Vandsburger

Also listed as: BIO ENG C265/NUC ENG C235

Principles of Magnetic Resonance Imaging: Read Less [-]

EL ENG 226A Random Processes in Systems 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Probability, random variables and their convergence, random processes. Filtering of wide sense stationary processes, spectral density, Wiener and Kalman filters. Markov processes and Markov chains. Gaussian, birth and death, poisson and shot noise processes. Elementary queueing analysis. Detection of signals in Gaussian and shot noise, elementary parameter estimation. Random Processes in Systems: Read More [+]

Prerequisites: EL ENG 120 and STAT 200A

Instructor: Anantharam

Formerly known as: 226

Random Processes in Systems: Read Less [-]

EL ENG 226B Applications of Stochastic Process Theory 2 Units

Terms offered: Spring 2017, Spring 2013, Spring 1997 Advanced topics such as: Martingale theory, stochastic calculus, random fields, queueing networks, stochastic control. Applications of Stochastic Process Theory: Read More [+]

Prerequisites: EL ENG 226A

Instructors: Anantharam, Varaiya

Applications of Stochastic Process Theory: Read Less [-]

EL ENG 227BT Convex Optimization 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Convex optimization is a class of nonlinear optimization problems where the objective to be minimized, and the constraints, are both convex. The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experiments with the optimization software CVX, and a discussion section. Convex Optimization: Read More [+]

Prerequisites: MATH 54 and STAT 2

Instructors: El Ghaoui, Wainwright

Convex Optimization: Read Less [-]

EL ENG C227C Convex Optimization and Approximation 3 Units

Terms offered: Spring 2022, Spring 2021, Spring 2020, Spring 2019, Spring 2018, Spring 2017 Convex optimization as a systematic approximation tool for hard decision problems. Approximations of combinatorial optimization problems, of stochastic programming problems, of robust optimization problems (i.e., with optimization problems with unknown but bounded data), of optimal control problems. Quality estimates of the resulting approximation. Applications in robust engineering design, statistics , control, finance, data mining, operations research. Convex Optimization and Approximation: Read More [+]

Prerequisites: 227A or consent of instructor

Also listed as: IND ENG C227B

Convex Optimization and Approximation: Read Less [-]

EL ENG C227T Introduction to Convex Optimization 4 Units

Terms offered: Prior to 2007 The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experience. Introduction to Convex Optimization: Read More [+]

Additional Format: Three hours of lecture and two hours of laboratory and one hour of discussion per week.

Formerly known as: Electrical Engineering C227A/Industrial Engin and Oper Research C227A

Also listed as: IND ENG C227A

Introduction to Convex Optimization: Read Less [-]

EL ENG 228A High Speed Communications Networks 3 Units

Terms offered: Fall 2014, Spring 2014, Fall 2011 Descriptions, models, and approaches to the design and management of networks. Optical transmission and switching technologies are described and analyzed using deterministic, stochastic, and simulation models. FDDI, DQDB, SMDS, Frame Relay, ATM, networks, and SONET. Applications demanding high-speed communication. High Speed Communications Networks: Read More [+]

Prerequisites: EL ENG 122 ; and EL ENG 226A (may be taken concurrently)

High Speed Communications Networks: Read Less [-]

EL ENG 229A Information Theory and Coding 3 Units

Terms offered: Fall 2024, Fall 2022, Fall 2021 Fundamental bounds of Shannon theory and their application. Source and channel coding theorems. Galois field theory, algebraic error-correction codes. Private and public-key cryptographic systems. Information Theory and Coding: Read More [+]

Prerequisites: STAT 200A ; and EL ENG 226 recommended

Instructors: Anantharam, Tse

Formerly known as: 229

Information Theory and Coding: Read Less [-]

EL ENG 229B Error Control Coding 3 Units

Terms offered: Spring 2019, Spring 2016, Fall 2013 Error control codes are an integral part of most communication and recording systems where they are primarily used to provide resiliency to noise. In this course, we will cover the basics of error control coding for reliable digital transmission and storage. We will discuss the major classes of codes that are important in practice, including Reed Muller codes, cyclic codes, Reed Solomon codes, convolutional codes, concatenated codes, turbo codes, and low density parity check codes. The relevant background material from finite field and polynomial algebra will be developed as part of the course. Overview of topics: binary linear block codes; Reed Muller codes; Galois fields; linear block codes over a finite field; cyclic codes; BCH and Reed Solomon codes; convolutional codes and trellis based decoding, message passing decoding algorithms; trellis based soft decision decoding of block codes; turbo codes; low density parity check codes. Error Control Coding: Read More [+]

Prerequisites: 126 or equivalent (some familiarity with basic probability). Prior exposure to information theory not necessary

Instructor: Anatharam

Error Control Coding: Read Less [-]

EL ENG 230A Integrated-Circuit Devices 4 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 Overview of electronic properties of semiconductors. Metal-semiconductor contacts, pn junctions, bipolar transistors, and MOS field-effect transistors. Properties that are significant to device operation for integrated circuits. Silicon device fabrication technology. Integrated-Circuit Devices: Read More [+]

Prerequisites: EECS 16A AND EECS 16B

Credit Restrictions: Students will receive no credit for EL ENG 230A after completing EL ENG 130 , EL ENG 230M, or EL ENG W230A . A deficient grade in EL ENG 230A may be removed by taking EL ENG W230A .

Formerly known as: Electrical Engineering 230M

Integrated-Circuit Devices: Read Less [-]

EL ENG 230B Solid State Devices 4 Units

Terms offered: Fall 2020, Spring 2019, Spring 2018 Physical principles and operational characteristics of semiconductor devices. Emphasis is on MOS field-effect transistors and their behaviors dictated by present and probable future technologies. Metal-oxide-semiconductor systems, short-channel and high field effects, device modeling, and impact on analog, digital circuits. Solid State Devices: Read More [+]

Prerequisites: EL ENG 130

Credit Restrictions: Students will receive no credit for EL ENG 230B after completing EL ENG 231, or EL ENG W230B . A deficient grade in EL ENG 230B may be removed by taking EL ENG W230B .

Instructors: Subramanian, King Liu, Salahuddin

Formerly known as: Electrical Engineering 231

Solid State Devices: Read Less [-]

EL ENG 230C Solid State Electronics 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2018 Crystal structure and symmetries. Energy-band theory. Cyclotron resonance. Tensor effective mass. Statistics of electronic state population. Recombination theory. Carrier transport theory. Interface properties. Optical processes and properties. Solid State Electronics: Read More [+]

Prerequisites: EL ENG 131; and PHYSICS 137B

Instructors: Bokor, Salahuddin

Formerly known as: Electrical Engineering 230

Solid State Electronics: Read Less [-]

EL ENG W230A Integrated-Circuit Devices 4 Units

Terms offered: Spring 2019, Spring 2018, Spring 2017 Overview of electronic properties of semiconductors. Metal-semiconductor contacts, pn junctions, bipolar transistors, and MOS field-effect transistors. Properties that are significant to device operation for integrated circuits. Silicon device fabrication technology. Integrated-Circuit Devices: Read More [+]

Prerequisites: MAS-IC students only

Credit Restrictions: Students will receive no credit for Electrical Engineering W230A after taking Electrical Engineering 130, Electrical Engineering W130 or Electrical Engineering 230A.

Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 1 hour of web-based discussion per week

Summer: 10 weeks - 4.5 hours of web-based lecture and 1.5 hours of web-based discussion per week

Additional Format: Three hours of Web-based lecture and One hour of Web-based discussion per week for 15 weeks. Four and one-half hours of Web-based lecture and One and one-half hours of Web-based discussion per week for 10 weeks.

Instructors: Javey, Subramanian, King Liu

Formerly known as: Electrical Engineering W130

EL ENG W230B Solid State Devices 4 Units

Terms offered: Fall 2015 Physical principles and operational characteristics of semiconductor devices. Emphasis is on MOS field-effect transistors and their behaviors dictated by present and probable future technologies. Metal-oxide-semiconductor systems, short-channel and high field effects, device modeling, and impact on analog, digital circuits. Solid State Devices: Read More [+]

Prerequisites: EL ENG W230A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W230B after taking EE 230B.

Formerly known as: Electrical Engineering W231

EL ENG 232 Lightwave Devices 4 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 This course is designed to give an introduction and overview of the fundamentals of optoelectronic devices. Topics such as optical gain and absorption spectra, quantization effects, strained quantum wells, optical waveguiding and coupling, and hetero p-n junction will be covered. This course will focus on basic physics and design principles of semiconductor diode lasers, light emitting diodes, photodetectors and integrated optics. Practical applications of the devices will be also discussed. Lightwave Devices: Read More [+]

Prerequisites: EL ENG 130 ; PHYSICS 137A ; and EL ENG 117 recommended

Instructor: Wu

Lightwave Devices: Read Less [-]

EL ENG 234A Fundamentals of Photovoltaic Devices 4 Units

Terms offered: Spring 2025 This course is designed to give an introduction, and overview of, the fundamentals of photovoltaic devices. Students will learn how solar cells work, understand the concepts and models of solar cell device physics, and formulate and solve relevant physical problems related to photovoltaic devices. Monocrystalline, thin film and third generation solar cells will be discussed and analyzed. Light management and economic considerations in a solar cell system will also be covered. Fundamentals of Photovoltaic Devices: Read More [+]

Prerequisites: EECS 16A and EECS 16B , or Math 54 and Physics 7B, or equivalent

Instructor: Arias

Fundamentals of Photovoltaic Devices: Read Less [-]

EL ENG C235 Nanoscale Fabrication 4 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023, Spring 2016, Spring 2015, Spring 2013 This course discusses various top-down and bottom-up approaches to synthesizing and processing nanostructured materials. The topics include fundamentals of self assembly, nano-imprint lithography, electron beam lithography, nanowire and nanotube synthesis, quantum dot synthesis (strain patterned and colloidal), postsynthesis modification (oxidation, doping, diffusion, surface interactions, and etching techniques). In addition, techniques to bridging length scales such as heterogeneous integration will be discussed. We will discuss new electronic, optical, thermal, mechanical, and chemical properties brought forth by the very small sizes. Nanoscale Fabrication: Read More [+]

Instructor: Chang-Hasnain

Also listed as: NSE C203

Nanoscale Fabrication: Read Less [-]

EL ENG 236A Quantum and Optical Electronics 3 Units

Terms offered: Fall 2023, Fall 2022, Spring 2021 Interaction of radiation with atomic and semiconductor systems, density matrix treatment, semiclassical laser theory (Lamb's), laser resonators, specific laser systems, laser dynamics, Q-switching and mode-locking, noise in lasers and optical amplifiers. Nonlinear optics, phase-conjugation, electrooptics, acoustooptics and magnetooptics, coherent optics, stimulated Raman and Brillouin scattering. Quantum and Optical Electronics: Read More [+]

Prerequisites: EL ENG 117A and PHYSICS 137A

Quantum and Optical Electronics: Read Less [-]

EL ENG C239 Partially Ionized Plasmas 3 Units

Terms offered: Spring 2010, Spring 2009, Spring 2007 Introduction to partially ionized, chemically reactive plasmas, including collisional processes, diffusion, sources, sheaths, boundaries, and diagnostics. DC, RF, and microwave discharges. Applications to plasma-assisted materials processing and to plasma wall interactions. Partially Ionized Plasmas: Read More [+]

Prerequisites: An upper division course in electromagnetics or fluid dynamics

Additional Format: Forty-five hours of lecture per term.

Formerly known as: 239

Also listed as: AST C239

Partially Ionized Plasmas: Read Less [-]

EL ENG 240A Analog Integrated Circuits 4 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 Single and multiple stage transistor amplifiers. Operational amplifiers. Feedback amplifiers, 2-port formulation, source, load, and feedback network loading. Frequency response of cascaded amplifiers, gain-bandwidth exchange, compensation, dominant pole techniques, root locus. Supply and temperature independent biasing and references. Selected applications of analog circuits such as analog-to-digital converters, switched capacitor filters, and comparators. Hardware laboratory and design project. Analog Integrated Circuits: Read More [+]

Prerequisites: EL ENG 105

Credit Restrictions: Students will receive no credit for EL ENG 240A after completing EL ENG 140 , or EL ENG W240A . A deficient grade in EL ENG 240A may be removed by taking EL ENG W240A .

Instructors: Sanders, Nguyen

Analog Integrated Circuits: Read Less [-]

EL ENG 240B Advanced Analog Integrated Circuits 4 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023 Analysis and optimized design of monolithic operational amplifiers and wide-band amplifiers; methods of achieving wide-band amplification, gain-bandwidth considerations; analysis of noise in integrated circuits and low noise design. Precision passive elements, analog switches, amplifiers and comparators, voltage reference in NMOS and CMOS circuits, Serial, successive-approximation, and parallel analog-to-digital converters. Switched-capacitor and CCD filters. Applications to codecs, modems. Advanced Analog Integrated Circuits: Read More [+]

Prerequisites: EL ENG 140 / EL ENG 240A

Credit Restrictions: Students will receive no credit for EL ENG 240B after completing EL ENG 240, or EL ENG W240B . A deficient grade in EL ENG 240B may be removed by taking EL ENG W240B .

Advanced Analog Integrated Circuits: Read Less [-]

EL ENG 240C Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits 3 Units

Terms offered: Fall 2024, Spring 2023, Fall 2019 Architectural and circuit level design and analysis of integrated analog-to-digital and digital-to-analog interfaces in CMOS and BiCMOS VLSI technology. Analog-digital converters, digital-analog converters, sample/hold amplifiers, continuous and switched-capacitor filters. RF integrated electronics including synthesizers, LNA's, and baseband processing. Low power mixed signal design. Data communications functions including clock recovery. CAD tools for analog design including simulation and synthesis. Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read More [+]

Prerequisites: EL ENG 140

Credit Restrictions: Students will receive no credit for EL ENG 240C after completing EL ENG 290Y , or EL ENG W240C . A deficient grade in EL ENG 240C may be removed by taking EL ENG W240C .

Instructor: Boser

Formerly known as: Electrical Engineering 247

Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read Less [-]

EL ENG W240A Analog Integrated Circuits 4 Units

Terms offered: Spring 2020, Spring 2019, Spring 2018 Single and multiple stage transistor amplifiers. Operational amplifiers. Feedback amplifiers, 2-port formulation, source, load, and feedback network loading. Frequency response of cascaded amplifiers, gain-bandwidth exchange, compensation, dominant pole techniques, root locus. Supply and temperature independent biasing and references. Selected applications of analog circuits such as analog-to-digital converters, switched capacitor filters , and comparators. Analog Integrated Circuits: Read More [+]

Credit Restrictions: Students will receive no credit for EE W240A after taking EE 140 or EE 240A.

Instructors: Alon, Sanders, Nguyen

EL ENG W240B Advanced Analog Integrated Circuits 3 Units

Terms offered: Spring 2020, Spring 2019, Fall 2015 Analysis and optimized design of monolithic operational amplifiers and wide-band amplifiers; methods of achieving wide-band amplification, gain-bandwidth considerations; analysis of noise in integrated circuits and low noise design. Precision passive elements, analog switches, amplifiers and comparators, voltage reference in NMOS and CMOS circuits, Serial, successive-approximation, and parallel analog-to-digital converts. Switched-capacitor and CCD filters. Applications to codecs, modems. Advanced Analog Integrated Circuits: Read More [+]

Prerequisites: EL ENG W240A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W240B after taking EE 240B.

Summer: 10 weeks - 4.5 hours of web-based lecture per week

Additional Format: Three hours of Web-based lecture per week for 15 weeks. Four and one-half hours of Web-based lecture per week for 10 weeks.

Formerly known as: Electrical Engineering W240

EL ENG W240C Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits 3 Units

Terms offered: Spring 2017, Spring 2016 Architectural and circuit level design and analysis of integrated analog-to-digital and digital-to-analog interfaces in modern CMOS and BiCMOS VLSI technology. Analog-digital converters, digital-analog converters, sample/hold amplifiers, continuous and switched-capacitor filters. Low power mixed signal design techniques. Data communications systems including interface circuity. CAD tools for analog design for simulation and synthesis. Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read More [+]

Credit Restrictions: Students will receive no credit for EE W240C after taking EE 240C.

Formerly known as: Electrical Engineering W247

EL ENG 241B Advanced Digital Integrated Circuits 3 Units

Terms offered: Spring 2021, Spring 2020, Spring 2019 Analysis and design of MOS and bipolar large-scale integrated circuits at the circuit level. Fabrication processes, device characteristics, parasitic effects static and dynamic digital circuits for logic and memory functions. Calculation of speed and power consumption from layout and fabrication parameters. ROM, RAM, EEPROM circuit design. Use of SPICE and other computer aids. Advanced Digital Integrated Circuits: Read More [+]

Prerequisites: EL ENG 141

Credit Restrictions: Students will receive no credit for EL ENG 241B after completing EL ENG 241, or EL ENG W241B . A deficient grade in EL ENG 241B may be removed by taking EL ENG W241B .

Instructors: Nikolic, Rabaey

Formerly known as: Electrical Engineering 241

Advanced Digital Integrated Circuits: Read Less [-]

EL ENG W241A Introduction to Digital Integrated Circuits 4 Units

Terms offered: Fall 2015, Fall 2014, Spring 2014 CMOS devices and deep sub-micron manufacturing technology. CMOS inverters and complex gates. Modeling of interconnect wires. Optimization of designs with respect to a number of metrics: cost, reliability, performance, and power dissipation. Sequential circuits, timing considerations, and clocking approaches. Design of large system blocks, including arithmetic, interconnect, memories, and programmable logic arrays. Introduction to design methodologies , including laboratory experience. Introduction to Digital Integrated Circuits: Read More [+]

Credit Restrictions: Students will receive no credit for W241A after taking EE 141 or EE 241A.

Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 4 hours of web-based discussion per week

Summer: 10 weeks - 4.5 hours of web-based lecture and 6 hours of web-based discussion per week

Additional Format: F/Sp: Three hours of web-based lecture, one hour of web-based discussion, and three hours of web-based laboratory per week. Su: Four and one-half hours of web-based lecture, one and one-half hours of web-based discussion, and four and one-half hours of web-based laboratory per week for ten weeks.

Instructors: Alon, Rabaey, Nikolic

Introduction to Digital Integrated Circuits: Read Less [-]

EL ENG W241B Advanced Digital Integrated Circuits 3 Units

Terms offered: Spring 2017, Spring 2016, Spring 2015 Analysis and design of MOS and bipolar large-scale integrated circuits at the circuit level. Fabrication processes, device characteristics, parasitic effects static and dynamic digital circuits for logic and memory functions. Calculation of speed and power consumption from layout and fabrication parameters. ROM, RAM, EEPROM circuit design. Use of SPICE and other computer aids. Advanced Digital Integrated Circuits: Read More [+]

Prerequisites: EL ENG W241A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W241B after taking EE 241B.

Formerly known as: Electrical Engineering W241

EL ENG 242A Integrated Circuits for Communications 4 Units

Terms offered: Spring 2025, Fall 2023, Spring 2023 Analysis and design of electronic circuits for communication systems, with an emphasis on integrated circuits for wireless communication systems. Analysis of noise and distortion in amplifiers with application to radio receiver design. Power amplifier design with application to wireless radio transmitters. Radio-frequency mixers, oscillators, phase-locked loops, modulators, and demodulators. Integrated Circuits for Communications: Read More [+]

Prerequisites: EL ENG 140 /240A or equivalent

Credit Restrictions: Students will receive no credit for Electrical Engineering 242A after taking Electrical Engineering 142.

Formerly known as: Electrical Engineering 242M

Integrated Circuits for Communications: Read Less [-]

EL ENG 242B Advanced Integrated Circuits for Communications 3 Units

Terms offered: Fall 2024, Fall 2020, Fall 2014 Analysis, evaluation and design of present-day integrated circuits for communications application, particularly those for which nonlinear response must be included. MOS, bipolar and BICMOS circuits, audio and video power amplifiers, optimum performance of near-sinusoidal oscillators and frequency-translation circuits. Phase-locked loop ICs, analog multipliers and voltage-controlled oscillators; advanced components for telecommunication circuits. Use of new CAD tools and systems. Advanced Integrated Circuits for Communications: Read More [+]

Prerequisites: EL ENG 142 and EL ENG 240

Credit Restrictions: Students will receive no credit for EL ENG 242B after completing EL ENG 242, or EL ENG W242B . A deficient grade in EL ENG 242B may be removed by taking EL ENG W242B .

Instructor: Niknejad

Formerly known as: Electrical Engineering 242

Advanced Integrated Circuits for Communications: Read Less [-]

EL ENG W242A Integrated Circuits for Communications 4 Units

Terms offered: Spring 2020, Spring 2019, Spring 2018 Analysis and design of electronic circuits for communication systems, with an emphasis on integrated circuits for wireless communication systems. Analysis of noise and distortion in amplifiers with application to radio receiver design. Power amplifier design with application to wireless radio transmitters. Radio-frequency mixers, oscillators, phase-locked loops, modulators, and demodulators. Integrated Circuits for Communications: Read More [+]

Credit Restrictions: Students will receive no credit for EE W242A after taking EE 142, EE 242A, or EE 242B.

Formerly known as: Electrical Engineering W142

EL ENG W242B Advanced Integrated Circuits for Communications 3 Units

Terms offered: Spring 2017, Spring 2016 Analysis, evaluation, and design of present-day integrated circuits for communications application, particularly those for which nonlinear response must be included. MOS, bipolar and BICMOS circuits, audio and video power amplifiers, optimum performance of near-sinusoidal oscillators and frequency-translation circuits. Phase-locked loop ICs, analog multipliers and voltage-controlled oscillators; advanced components for telecommunication circuits. Use of new CAD tools and systems. Advanced Integrated Circuits for Communications: Read More [+]

Prerequisites: EL ENG W240A ; EL ENG W242A ; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W242B after taking EE 242B.

Formerly known as: Electrical Engineering W242

EL ENG 243 Advanced IC Processing and Layout 3 Units

Terms offered: Spring 2014, Spring 2012, Spring 2011 The key processes for the fabrication of integrated circuits. Optical, X-ray, and e-beam lithography, ion implantation, oxidation and diffusion. Thin film deposition. Wet and dry etching and ion milling. Effect of phase and defect equilibria on process control. Advanced IC Processing and Layout: Read More [+]

Prerequisites: EL ENG 143 ; and either EL ENG 140 or EL ENG 141

Advanced IC Processing and Layout: Read Less [-]

EL ENG 244 Fundamental Algorithms for Systems Modeling, Analysis, and Optimization 4 Units

Terms offered: Spring 2025, Fall 2016, Fall 2015 The modeling, analysis, and optimization of complex systems requires a range of algorithms and design software. This course reviews the fundamental techniques underlying the design methodology for complex systems, using integrated circuit design as example. Topics include design flows, discrete and continuous models and algorithms, and strategies for implementing algorithms efficiently and correctly in software. Laboratory assignments and a class project will expose students to state-of-the-art. Fundamental Algorithms for Systems Modeling, Analysis, and Optimization: Read More [+]

Prerequisites: Graduate standing

Credit Restrictions: Students will receive no credit for EL ENG 244 after completing EL ENG W244 .

Instructors: Keutzer, Lee, Roychowdhury, Seshia

Fundamental Algorithms for Systems Modeling, Analysis, and Optimization: Read Less [-]

EL ENG W244 Fundamental Algorithms for System Modeling, Analysis, and Optimization 4 Units

Terms offered: Fall 2015 The modeling, analysis, and optimization of complex systems require a range of algorithms and design tools. This course reviews the fundamental techniques underlying the design methodology for complex systems, using integrated circuit design as an example. Topics include design flows, discrete and continuous models and algorithms, and strategies for implementing algorithms efficiently and correctly in software. Fundamental Algorithms for System Modeling, Analysis, and Optimization: Read More [+]

Credit Restrictions: Students will receive no credit for W244 after taking 144 and 244.

Fundamental Algorithms for System Modeling, Analysis, and Optimization: Read Less [-]

EL ENG C246 Parametric and Optimal Design of MEMS 3 Units

Terms offered: Spring 2013, Spring 2012, Spring 2011 Parametric design and optimal design of MEMS. Emphasis on design, not fabrication. Analytic solution of MEMS design problems to determine the dimensions of MEMS structures for specified function. Trade-off of various performance requirements despite conflicting design requirements. Structures include flexure systems, accelerometers, and rate sensors. Parametric and Optimal Design of MEMS: Read More [+]

Prerequisites: Graduate standing or consent of instructor

Instructors: Lin, Pisano

Formerly known as: 219

Also listed as: MEC ENG C219

Parametric and Optimal Design of MEMS: Read Less [-]

EL ENG 247A Introduction to Microelectromechanical Systems (MEMS) 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course will teach fundamentals of micromachining and microfabrication techniques, including planar thin-film process technologies, photolithographic techniques, deposition and etching techniques, and the other technologies that are central to MEMS fabrication. It will pay special attention to teaching of fundamentals necessary for the design and analysis of devices and systems in mechanical, electrical, fluidic, and thermal energy/signal domains , and will teach basic techniques for multi-domain analysis. Fundamentals of sensing and transduction mechanisms including capacitive and piezoresistive techniques, and design and analysis of micmicromachined miniature sensors and actuators using these techniques will be covered. Introduction to Microelectromechanical Systems (MEMS): Read More [+]

Prerequisites: EECS 16A and EECS 16B ; or consent of instructor required

Credit Restrictions: Students will receive no credit for EE 247A after taking EE 147.

Instructors: Maharbiz, Nguyen, Pister

Introduction to Microelectromechanical Systems (MEMS): Read Less [-]

EL ENG C247B Introduction to MEMS Design 4 Units

Terms offered: Spring 2025, Spring 2024, Spring 2023, Spring 2022, Spring 2021 Physics, fabrication, and design of micro-electromechanical systems (MEMS). Micro and nanofabrication processes, including silicon surface and bulk micromachining and non-silicon micromachining. Integration strategies and assembly processes. Microsensor and microactuator devices: electrostatic, piezoresistive, piezoelectric, thermal, magnetic transduction. Electronic position-sensing circuits and electrical and mechanical noise. CAD for MEMS. Design project is required. Introduction to MEMS Design: Read More [+]

Prerequisites: Graduate standing in engineering or science; undergraduates with consent of instructor

Instructors: Nguyen, Pister

Formerly known as: Electrical Engineering C245, Mechanical Engineering C218

Also listed as: MEC ENG C218

Introduction to MEMS Design: Read Less [-]

EL ENG W247B Introduction to MEMS Design 4 Units

Terms offered: Prior to 2007 Physics, fabrication and design of micro electromechanical systems (MEMS). Micro and nano-fabrication processes, including silicon surface and bulk micromachining and non-silicon micromachining. Integration strategies and assembly processes. Microsensor and microactuator devices: electrostatic, piezoresistive, piezoelectric, thermal, and magnetic transduction. Electronic position-sensing circuits and electrical and mechanical noise. CAD for MEMS. Design project is required. Introduction to MEMS Design: Read More [+]

Credit Restrictions: Students will receive no credit for EE W247B after taking EE C247B or Mechanical Engineering C218.

Formerly known as: Electrical Engineering W245

EL ENG 248C Numerical Modeling and Analysis: Nonlinear Systems and Noise 4 Units

Terms offered: Prior to 2007 Numerical modelling and analysis techniques are widely used in scientific and engineering practice; they are also an excellent vehicle for understanding and concretizing theory. This course covers topics important for a proper understanding of nonlinearity and noise: periodic steady state and envelope ("RF") analyses; oscillatory systems; nonstationary and phase noise; and homotopy/continuation techniques for solving "difficult" equation systems. An underlying theme of the course is relevance to different physical domains, from electronics (e.g., analog/RF/mixed-signal circuits, high-speed digital circuits, interconnect, etc.) to optics, nanotechnology, chemistry, biology and mechanics. Hands-on coding using the MATLAB-based Berkeley Model Numerical Modeling and Analysis: Nonlinear Systems and Noise: Read More [+]

Course Objectives: Homotopy techniques for robust nonlinear equation solution Modelling and analysis of oscillatory systems - harmonic, ring and relaxation oscillators - oscillator steady state analysis - perturbation analysis of amplitude-stable oscillators RF (nonlinear periodic steady state) analysis - harmonic balance and shooting - Multi-time PDE and envelope methods - perturbation analysis of periodic systems (Floquet theory) RF (nonlinear, nonstationary) noise concepts and their application - cyclostationary noise analysis - concepts of phase noise in oscillators Using MAPP for fast/convenient modelling and analysis

Student Learning Outcomes: Students will develop a facility in the above topics and be able to apply them widely across science and engineering.

Prerequisites: Consent of Instructor

Numerical Modeling and Analysis: Nonlinear Systems and Noise: Read Less [-]

EL ENG C249A Introduction to Embedded Systems 4 Units

Also listed as: COMPSCI C249A

EL ENG C261 Medical Imaging Signals and Systems 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Biomedical imaging is a clinically important application of engineering, applied mathematics, physics, and medicine. In this course, we apply linear systems theory and basic physics to analyze X-ray imaging, computerized tomography, nuclear medicine, and MRI. We cover the basic physics and instrumentation that characterizes medical image as an ideal perfect-resolution image blurred by an impulse response. This material could prepare the student for a career in designing new medical imaging systems that reliably detect small tumors or infarcts. Medical Imaging Signals and Systems: Read More [+]

Course Objectives: • understand how 2D impulse response or 2D spatial frequency transfer function (or Modulation Transfer Function) allow one to quantify the spatial resolution of an imaging system. • understand 2D sampling requirements to avoid aliasing • understand 2D filtered backprojection reconstruction from projections based on the projection-slice theorem of Fourier Transforms • understand the concept of image reconstruction as solving a mathematical inverse problem. • understand the limitations of poorly conditioned inverse problems and noise amplification • understand how diffraction can limit resolution---but not for the imaging systems in this class • understand the hardware components of an X-ray imaging scanner • • understand the physics and hardware limits to spatial resolution of an X-ray imaging system • understand tradeoffs between depth, contrast, and dose for X-ray sources • understand resolution limits for CT scanners • understand how to reconstruct a 2D CT image from projection data using the filtered backprojection algorithm • understand the hardware and physics of Nuclear Medicine scanners • understand how PET and SPECT images are created using filtered backprojection • understand resolution limits of nuclear medicine scanners • understand MRI hardware components, resolution limits and image reconstruction via a 2D FFT • understand how to construct a medical imaging scanner that will achieve a desired spatial resolution specification.

Student Learning Outcomes: • students will be tested for their understanding of the key concepts above • undergraduate students will apply to graduate programs and be admitted • students will apply this knowledge to their research at Berkeley, UCSF, the national labs or elsewhere • students will be hired by companies that create, sell, operate or consult in biomedical imaging

Prerequisites: Undergraduate level course work covering integral and differential calculus, two classes in engineering-level physics, introductory level linear algebra, introductory level statistics, at least 1 course in LTI system theory including (analog convolution, Fourier transforms, and Nyquist sampling theory). The recommended undergrad course prerequisites are introductory level skills in Python or Matlab and either EECS 16A , EECS 16B and EL ENG 120 , or MATH 54 , BIO ENG 101 , and BIO ENG 105

Instructor: Conolly

Also listed as: BIO ENG C261/NUC ENG C231

Medical Imaging Signals and Systems: Read Less [-]

EL ENG 290 Advanced Topics in Electrical Engineering 1 - 4 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Read More [+]

Repeat rules: Course may be repeated for credit when topic changes.

Additional Format: One to three hours of lecture per week. Two to five hours of lecture per week for 10 weeks. Two to six hours of lecture per week for 8 weeks. Three to nine hours of lecture per week for 6 weeks. Three to fifteen hours of lecture per week for four weeks.

Advanced Topics in Electrical Engineering: Read Less [-]

EL ENG 290A Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design 1 - 3 Units

Terms offered: Spring 2016, Spring 2015, Fall 2014 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design: Read More [+]

Fall and/or spring: 15 weeks - 1-3 hours of lecture per week

Additional Format: One to Three hour of Lecture per week for 15 weeks.

Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design: Read Less [-]

EL ENG 290B Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices 1 - 3 Units

Terms offered: Spring 2021, Spring 2020, Spring 2019 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices: Read Less [-]

EL ENG 290C Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design 1 - 3 Units

Terms offered: Spring 2019, Fall 2018, Spring 2018 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design: Read Less [-]

EL ENG 290D Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology 1 - 3 Units

Terms offered: Spring 2021, Fall 2014, Fall 2013 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology: Read Less [-]

EL ENG 290F Advanced Topics in Electrical Engineering: Advanced Topics in Photonics 1 - 3 Units

Terms offered: Spring 2014, Fall 2013, Fall 2012 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Photonics: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Photonics: Read Less [-]

EL ENG 290G Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators 1 - 3 Units

Terms offered: Fall 2017, Fall 2016, Spring 2002 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators: Read More [+]

Formerly known as: Engineering 210

Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators: Read Less [-]

EL ENG 290N Advanced Topics in Electrical Engineering: Advanced Topics in System Theory 1 - 3 Units

Terms offered: Fall 2018, Fall 2017, Fall 2015 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in System Theory: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in System Theory: Read Less [-]

EL ENG 290O Advanced Topics in Electrical Engineering: Advanced Topics in Control 1 - 3 Units

Terms offered: Spring 2019, Fall 2018, Fall 2017 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Control: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Control: Read Less [-]

EL ENG 290P Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics 1 - 3 Units

Terms offered: Spring 2019, Spring 2018, Fall 2017 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics: Read Less [-]

EL ENG 290Q Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks 1 - 3 Units

Terms offered: Spring 2017, Spring 2016, Fall 2014 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks: Read Less [-]

EL ENG 290S Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory 1 - 3 Units

Terms offered: Fall 2018, Fall 2016, Fall 2009 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory: Read Less [-]

EL ENG 290T Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing 1 - 3 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016 The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester. Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing: Read More [+]

Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing: Read Less [-]

EL ENG 290Y Advanced Topics in Electrical Engineering: Organic Materials in Electronics 3 Units

Terms offered: Spring 2014, Spring 2013, Fall 2009 Organic materials are seeing increasing application in electronics applications. This course will provide an overview of the properties of the major classes of organic materials with relevance to electronics. Students will study the technology, physics, and chemistry of their use in the three most rapidly growing major applications--energy conversion/generation devices (fuel cells and photovoltaics), organic light-emitting diodes, and organic transistors. Advanced Topics in Electrical Engineering: Organic Materials in Electronics: Read More [+]

Prerequisites: EL ENG 130 ; and undergraduate general chemistry

Instructor: Subramanian

Advanced Topics in Electrical Engineering: Organic Materials in Electronics: Read Less [-]

EL ENG W290C Advanced Topics in Circuit Design 3 Units

Terms offered: Prior to 2007 Seminar-style course presenting an in-depth perspective on one specific domain of integrated circuit design. Most often, this will address an application space that has become particularly relevant in recent times. Examples are serial links, ultra low-power design, wireless transceiver design, etc. Advanced Topics in Circuit Design: Read More [+]

Credit Restrictions: Students will receive no credit for W290C after taking 290C.

Advanced Topics in Circuit Design: Read Less [-]

EL ENG C291 Control and Optimization of Distributed Parameters Systems 3 Units

Terms offered: Fall 2017, Spring 2016, Spring 2015, Spring 2014 Distributed systems and PDE models of physical phenomena (propagation of waves, network traffic, water distribution, fluid mechanics, electromagnetism, blood vessels, beams, road pavement, structures, etc.). Fundamental solution methods for PDEs: separation of variables, self-similar solutions, characteristics, numerical methods, spectral methods. Stability analysis. Adjoint-based optimization. Lyapunov stabilization. Differential flatness. Viability control. Hamilton-Jacobi-based control. Control and Optimization of Distributed Parameters Systems: Read More [+]

Prerequisites: ENGIN 7 and MATH 54 ; or consent of instructor

Also listed as: CIV ENG C291F/MEC ENG C236

Control and Optimization of Distributed Parameters Systems: Read Less [-]

EL ENG C291E Hybrid Systems and Intelligent Control 3 Units

Terms offered: Spring 2021, Spring 2020, Spring 2018 Analysis of hybrid systems formed by the interaction of continuous time dynamics and discrete-event controllers. Discrete-event systems models and language descriptions. Finite-state machines and automata. Model verification and control of hybrid systems. Signal-to-symbol conversion and logic controllers. Adaptive, neural, and fuzzy-control systems. Applications to robotics and Intelligent Vehicle and Highway Systems (IVHS). Hybrid Systems and Intelligent Control: Read More [+]

Formerly known as: 291E

Also listed as: MEC ENG C290S

Hybrid Systems and Intelligent Control: Read Less [-]

EL ENG 297 Field Studies in Electrical Engineering 0 - 12 Units

Terms offered: Summer 2024 8 Week Session, Fall 2023, Summer 2023 8 Week Session Supervised experience in off-campus companies relevant to specific aspects and applications of electrical engineering. Written report required at the end of the semester. Field Studies in Electrical Engineering: Read More [+]

Summer: 8 weeks - 1-12 hours of independent study per week

Additional Format: Individual conferences. Individual conferences.

Field Studies in Electrical Engineering: Read Less [-]

EL ENG 298 Group Studies, Seminars, or Group Research 1 - 4 Units

Terms offered: Spring 2023, Spring 2022, Spring 2021 Advanced study in various subjects through special seminars on topics to be selected each year, informal group studies of special problems, group participation in comprehensive design problems, or group research on complete problems for analysis and experimentation. Group Studies, Seminars, or Group Research: Read More [+]

Fall and/or spring: 15 weeks - 0 hours of lecture per week

Additional Format: One to four hours of lectures per unit.

Group Studies, Seminars, or Group Research: Read Less [-]

EL ENG 299 Individual Research 1 - 12 Units

Terms offered: Fall 2024, Summer 2024 10 Week Session, Summer 2023 10 Week Session Investigation of problems in electrical engineering. Individual Research: Read More [+]

Summer: 6 weeks - 2.5-30 hours of independent study per week 8 weeks - 1.5-22.5 hours of independent study per week

Additional Format: Independent, individual study or investigation. Independent, individual study or investigation. Forty-five hours of work per unit per term.

EL ENG 375 Teaching Techniques for Electrical Engineering 2 Units

Terms offered: Spring 2025, Fall 2024, Spring 2024 Discussion of effective teaching techniques. Use of educational objectives, alternative forms of instruction, and proven techniques to enhance student learning. This course is intended to orient new student instructors to more effectively teach courses offered by the Department of Electrical Engineering and Computer Sciences at UC Berkeley. Teaching Techniques for Electrical Engineering: Read More [+]

Prerequisites: Teaching assistant or graduate student

Fall and/or spring: 15 weeks - 1.5 hours of seminar per week

Additional Format: One and one-half hours of seminar per week.

Subject/Course Level: Electrical Engineering/Professional course for teachers or prospective teachers

Teaching Techniques for Electrical Engineering: Read Less [-]

EL ENG 602 Individual Study for Doctoral Students 1 - 8 Units

Terms offered: Fall 2016, Fall 2015, Fall 2014 Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. (and other doctoral degrees). Individual Study for Doctoral Students: Read More [+]

Additional Format: Forty-five hours of work per unit per term. Independent study, in consultation with faculty member.

Subject/Course Level: Electrical Engineering/Graduate examination preparation

Contact Information

Department of electrical engineering and computer sciences.

231 Cory Hall

Phone: 510-642-1042

Fax: 510-642-5775

Vice Chair, Graduate Study and Prelims

Ana Claudia Arias, PhD

508 Cory Hall

[email protected]

John Wawrzynek, PhD

631 Soda Hall

[email protected]

Vice Chair, Masters’ Degree Programs (MEng & MS)

Alvin Cheung, PhD

785 Soda Hall

[email protected]

EECS Department Chair

Claire Tomlin, PhD

[email protected]

EECS Associate Chair/CS Division Chair

Jelani Nelson, PhD

633 Soda Hall

[email protected]

Executive Director, EECS Student Affairs

Susanne Kauer, Ed.M

221 Cory Hall

[email protected]

Director of Grad Matters, EE Grad Advisor

Judy I. Smithson, M.Ed.

217 Cory Hall

Phone: 510-643-8347

[email protected]

CS Graduate Student Advisor

367 Soda Hall

Phone: 510-642-9413

[email protected]

Masters' Student Advisor

Michael Sun

215 Cory Hall

[email protected]

CS Graduate Admissions and GSI Recruitment

Glenna Anton, PhD

Phone: 510-642-6285

[email protected]

Graduate Admissions and EE GSI Recruitment

Phone: 510-642-9265

[email protected]

Graduate Student Advisor

Tiffany Sun

253 Cory Hall

[email protected]

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Ph.D. Admissions

Next start date: August 2025

Application Deadline: December 3, 2024, 8:59 pm PST

We welcome students from a diverse set of backgrounds; some will be technically educated, some educated in the humanities and social sciences.

All application materials must be received by the deadline. We encourage you to apply early. The I School’s Ph.D. program does not accept applications for spring term admissions.

Admissions Requirements

• A bachelor’s degree or its recognized equivalent from an accredited institution
• Superior scholastic record, normally well above a 3.0 GPA
• Indication of appropriate research goals, described in the Statement of Purpose
• For applicants whose academic work has been in a language other than English, the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS)
• Not required: GRE/GMAT . Starting Fall 2021, we no longer require the GRE or GMAT. We recommend you put your time and effort towards the required application materials. Read more about our decision to drop the GRE/GMAT requirement .

Selection Criteria

The I School accepts 3–7 Ph.D. students each year from more than 100 applications. Applications are reviewed by a committee of faculty.

Applicants are evaluated holistically on a number of factors. A strong academic record is important, but not sufficient. A critical factor is the ability to demonstrate a research record and agenda that fit well with specific I School faculty. In a small, interdisciplinary program, it is important that applicants clearly indicate in their Statement of Purpose which faculty member(s) they are interested in researching with, and why.

Application Requirements

We encourage you to check out our Ph.D. Admissions FAQ for information about commonly asked application questions.

(1) Statement of Purpose & Personal History Essay

The Statement of Purpose and Personal History are two separate essays.

The Statement of Purpose should describe your aptitude and motivation for doctoral study in your area of specialization, including your preparation for this field of study, your academic plans and research interests, and your future goals. Please be sure to identify in your Statement of Purpose which faculty member(s) you are interested in researching with, and why. We expect that candidates are able to demonstrate a research record and agenda that fit well with specific I School faculty.

For additional guidance, please review the Graduate Division's Statement of Purpose Guide .

In addition to explaining how your personal experiences have influenced your decision to pursue graduate studies, your Personal History Essay may include any relevant information describing barriers to accessing higher education that you have overcome, efforts you have made to advance equitable access to higher education for women, racial minorities, and other groups historically underrepresented in higher education, or research that you have undertaken that focuses on underserved populations or related issues of inequality.

For additional guidance, please review the Graduate Division’s Personal Statement Guide . There is no minimum length for the Personal History Essay.

These two essays are used in part to evaluate the candidate’s writing skills. Pursuant to UC Berkeley policy, the statements must be written by the candidate her or himself. For admitted students, application materials must comply with the Code of Student Conduct .

Both essays should be uploaded as PDF documents, as part of the online application .

(2) Three Letters of Recommendation

Ph.D. applicants should provide letters which speak directly to their ability and potential to perform academic research at the doctoral level. Recommenders must submit their letters online; please follow the instructions in the online application .

(3) Current Curriculum Vitae

Please upload a current curriculum vitae (C.V.) as a PDF document as part of the online application .

(4) College Transcripts

As part of the online application, upload copies of the official transcripts or academic records for all university-level studies you have completed abroad and at U.S. institutions. Be sure to include a current transcript from every post-secondary school that you have attended, including community colleges, summer sessions, and extension programs.

Each transcript should be uploaded as a separate PDF document; please refer to the instructions on the online application .

Applicants who completed their undergraduate degree in a recognized academic institution outside the United States are required to upload a copy of their degree conferral certificate. If a degree conferral certificate has not yet been obtained, please upload a provisional certificate. Applicants who have not yet graduated from undergrad are not required to submit a provisional certificate at this time. For specific questions, please contact the School of Information at [email protected] .

(5) TOEFL or IELTS Scores

UC Berkeley Graduate Division requires that applicants who received their degrees in countries other than the U.S., U.K., Australia, or English-speaking Canada submit TOEFL (Test of English as a Foreign Language) or IELTS (International English Language Testing System) scores. This includes applicants with degrees from Bangladesh, Burma, Nepal, India, Pakistan, Latin America, the Middle East, North Africa, the People’s Republic of China, Taiwan, Japan, Korea, Southeast Asia, and most European countries. Only applicants who have completed a full year of U.S. university-level coursework with a grade of B or better are exempt from this requirement.

For students taking the TOEFL, UC Berkeley Graduate Division requires that your most recent score be at least 90 on the Internet-based version of the TOEFL.

For students taking the IELTS, UC Berkeley Graduate Division requires that your most recent score be at least 7.0 out of 9.0 on the IELTS Academic test.

UC Berkeley Graduate Division does not accept TOEFL ITP Plus for Mainland China, IELTS Indicator, or Duolingo scores. For more information, see  Graduate Division’s Evidence of English Language Proficiency .

Submitting Scores

To be valid, the TOEFL or IELTS must have been taken within the past 18 months: for applicants for Fall 2025 admission, test scores taken before June 2023 will not be accepted. Please have your test scores sent directly to UC Berkeley by the testing authorities prior to application submission, and at the latest, by the application deadline. It may take 10-15 days for official score reports to transfer to our system. For the TOEFL exam, the school code for UC Berkeley is 4833, and the department code for the I School is 99.

For the IELTS exam, please submit an electronic report from the testing center; no institution code is required. Here is the Graduate Division’s office address for identification purposes: University of California, Berkeley, Graduate Division, Sproul Hall Rm 318, MC 5900, Berkeley, CA 94720.

More information: TOEFL website ; IELTS website

(6) Application Fee

(submitted with the online application)

• Fee for domestic applicants: $135.
• Fee for international applicants: $155.

Application Fee Waiver : The I School is pleased to offer application fee waivers to eligible Ph.D. applicants. Prior to submitting your application, please complete our Application Fee Waiver request form , and we will contact you within two business days with further instructions.

All application materials must be received by the application deadline. Applications will be reviewed throughout December and January, and admissions decisions will be released by early February.

Please don’t hesitate to contact us with questions or for additional guidance: [email protected] or (510) 664-4742.

*Test Report Form must be sent directly from IELTS. IELTS Indicator scores are not accepted.

Computer Ownership Requirement

We require that students own a computer. No particular configuration or operating system is required. However, students will be expected to complete assignments using office productivity software (e.g., Microsoft Office, OpenOffice, etc.), web browsers, etc., and should own a computer capable of running such software. More specific guidance will be provided upon acceptance to the program.

Contact the admissions team with questions about the Ph.D. program or application.

Ph.D. Applicant Feedback Program

The I School Ph.D. Applicant Feedback Program is a student-run initiative that aims to assist underrepresented students with their application essays and C.V. as they apply to the UC Berkeley School of Information Ph.D. program.

More Information

• Ph.D. Admissions FAQ

Graduate Research Degree Programs (M.S./Ph.D.)

The Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) programs emphasize research preparation and experience.

Please note that Berkeley EECS no longer requires, nor accepts, GRE scores.

The Fall 2024 Admission application is now open

The Fall 2025 Graduate Admission application for EECS is now open. Interested applicants must submit an official application through the UC Berkeley Graduate Division website .  The deadline to apply is December 9, 2024, at 8:59 pm PST.

When choosing between our degree programs (M.S., M.S./Ph.D., Ph.D.), please carefully consider the descriptions at the bottom of this page.

New Faculty 2025

The following new faculty are joining UC Berkeley EECS in Fall 2025 and are taking new PhD students:

More information about new faculty can be found on the EECS New Faculty website .

To indicate one or more of these new faculty as your potential advisor/faculty mentor, please enter their name(s) in the field indicated below under the “Faculty Advisor” section of the application:

Application Prerequisites for All Graduate Research Degree Programs

The minimum graduate admission requirements are:

• A bachelor’s degree or recognized equivalent from an accredited institution . If you are in your final year of studies, and you expect to earn your degree by mid-August of the following year, you may apply. If you are admitted, you will be required to provide proof at that time that you have earned your bachelor’s degree, usually in the form of a final official transcript.
• If you attended a university that uses a 4.0 grade-point average (GPA) scale, a satisfactory scholastic average with a minimum GPA of 3.0 (B) is required. If you attended a university that does not use the 4.0 GPA scale, please do not try to convert your grades to the 4.0 scale for the application.
• Three Letters of Recommendation uploaded as PDFs through the link provided in the online application. Your letters could include details about your goals, research accomplishments, technical and leadership skills, academic work, etc. We suggest you give your recommenders at least two months to write your letters.
• If you received or are a candidate to receive a degree from an institution outside the United States , please see Minimum Degree Requirements for International Applicants and Evidence of English Language Proficiency .

*The GRE is no longer required nor accepted.

Research Areas

You can apply for the M.S., M.S./Ph.D., or Ph.D. program in either Electrical Engineering (EECS) or Computer Science (CS) .  To apply you will need to choose a division.  The table below shows which specialization areas fall under the EE division, under the CS division, and under both.

**Please note that on the official Grad Division application page EE is referred to as Electrical Engineering and Computer Sciences (EECS) and CS is referred to as Computer Science (CS) .  EE applicants must make sure to apply to EECS, and CS applicants make sure to apply to CS.

Electrical Engineering

Information, Data, Network, and Communication Sciences (IDNCS)

Computer Vision and Artificial Intelligence (CVAI)

Control, Intelligent Systems, and Robotics (CIR)

Integrated Circuits (INC)

Micro/Nano Electro Mechanical Systems (MEMS)

Physical Electronics (PHY)

Signal Processing (SP)

Biosystems & Computational Biology (BIO)

Computer Architecture & Engineering (ARC)

Cyber-Physical Systems and Design Automation (CPSDA)

Energy (ENE)

Computer Science

Artificial Intelligence (AI)

Database Management Systems (DBMS)

Education (EDUC)

Graphics (GR)

Human-Computer Interaction (HCI)

Operating Systems & Networking (OSNT)

Programming Systems (PS)

Scientific Computing (SCI)

Security (SEC)

Theory (THY)

Admissions Checklist

• Open an online application.
• Proof of English Proficiency – If your previous degree is from a university in a non-English speaking country, then you need to take the TOEFL or IELTS exam by November. TOEFL scores will remain valid for up to two years .  Send your scores electronically to Institution Code 4833.
• Statement of Purpose – Why are you applying for this program? What are your research goals and interests? What do you hope to accomplish in this degree program? What do you want to do after and how will this help you?
• Personal History Statement – What from your past made you decide to go into this field? And how will your personal history help you succeed in this program and your future goals?
• 3 Letters of Recommendation – Letters from professors are highly preferred. Letters could include details about your goals, research accomplishments, technical and leadership skills, academic work, etc. We suggest you give your recommenders at least two months to write your letters and upload them as PDFs through the link (provided from your online application).
• Unofficial Transcripts – Upload unofficial copies/PDFs of your transcripts from all the higher education institutions you attended. Hard copy or emailed transcripts will not be accepted. Transcripts not in English or Spanish must be translated prior to uploading to the application. If you graduated from a university outside of the U.S. you should also upload a copy of your degree certificate if your transcripts do not show the award of your degree.
• GPA – If you attended a university that does not use a 4.0 GPA scale, please enter your GPA as listed on your transcript in the section “Other Scale GPA.”
• Resume/CV – List any education, work, research, volunteer experience, awards, or future coursework.
• Pay the application fee. Eligible US citizens and Permanent Residents may apply in advance for application  waivers .
• Submit your completed application by the deadline. To avoid any technical issues, we recommend submitting your application well before the deadline. Late applications or materials will not be accepted.

Please review our Frequently Asked Questions for more information.

Application & Enrollment Timeline

Master of science (m.s.) only*.

Full-time Commitment: ~2 years

Focus: Leads to career in industrial R&D or Ph.D.

The MS only degree is intended mostly for currently registered UC Berkeley Ph.D. students that want to add the degree. Occasionally we admit exceptional applicants with research experience, but the cohort generally is limited to less than 10. Students interested in a research oriented degree should consider applying directly to the MS/PhD program.

If you are planning to immediately join the engineering profession without pursuing a Ph.D., the Master of Engineering (M.Eng.) is specifically designed as a professional master’s degree.

Doctor of Philosophy (Ph.D.) Only

Full-time Commitment: 5-6 years (starting with a Bachelor’s),  3-5 years (starting with a Master’s)

Focus: Research + teaching experience

A Ph.D. in EECS combines coursework and original research with exceptional faculty mentoring to  prepare students for a career in academia or industry.

The EECS Department offers two types of Ph.D. degrees (EE and CS). The principal requirements for the Ph.D. are:

• coursework (a major field and two minor fields)
• departmental preliminary requirement (an oral exam and breadth courses, which are different for EE and CS)
• the qualifying exam
• the dissertation

* The Master of Science (M.S.) Only program is a very small research program for exceptional applicants with research experience.  Applicants should consider applying to the MS/PhD program, since there are very few students in the M.S. only program.

Equal Access to Application Assistance Program

The student-run Equal Access Assistance (EAAA) program aims to ensure that all applicants to higher degree programs (M.S./Ph.D.) at Berkeley EECS have access to guidance on the higher degree application process. One current (or recent graduate) in EECS will provide feedback on your statement of purpose, personal history statement, CV/resume, and other application materials in advance of higher-degree application deadlines in the fall semester.  This feedback will be Berkeley- and admissions-focused, as opposed to grammar or formatting advice.

Application for admission to Berkeley EECS is a process that must be completed separately; participation in EAAA does not guarantee admission nor affect the admissions decision-making process in any way.

More information about the EAAA