There are two ways to study Computer Science (CS) at UC Berkeley:
Be admitted to the Electrical Engineering & Computer Sciences (EECS) major in the College of Engineering (COE) as a first year or junior transfer. Admission to the COE, however, is extremely competitive. This option leads to a Bachelor of Science (BS) degree. This path is appropriate for people who want an engineering education.
Be admitted to the Computer Science (CS) major in the College of Computing, Data Science, and Society (CDSS) as a first year or junior transfer. Admission to the CS major in the College of CDSS, however, is extremely competitive. This option leads to a Bachelor of Arts (BA) degree. This path is appropriate for people who are interested in a broader education in the sciences and arts.
There is no difference in the CS course content between the BS and BA programs. The difference is in what else you take: mainly engineering, or mainly humanities and social sciences. In particular, an interest in hardware suggests the EECS route.
Note: Beginning Fall 2024, students will no longer be able to apply to the Computer Science major in the College of Letters and Science on the UC Berkeley application. Going forward, students will need to apply to the Computer Science major in the College of CDSS.
This CS major is for students enrolled in the College of Computing, Data Science, and Society (CDSS). Berkeley emphasizes the science of computer science, which means much more than just computer programming. It includes the theory of computation, the design and analysis of algorithms, the architecture and logic design of computers, programming languages, compilers, operating systems, scientific computation, computer graphics, databases, artificial intelligence, and natural language processing. Our goal is to prepare students both for a possible research career and long-term technical leadership in industry. We must therefore look beyond today's technology and give students the primary ideas and the learning skills that will prepare them to teach themselves about tomorrow's technology.
Five-Year BS/MS Program
This program is geared toward students who would like to pursue an education beyond the BS/BA, allowing them to achieve greater breadth and/or depth of knowledge, and who would like to try their hand at research as well. For information, please see the Five-Year BS/MS Requirements tab on this page.
Honors Program
Computer Science majors with an overall GPA of 3.70 or above are eligible to apply to the EECS honors degree program.
Minor Program
A minor in Computer Science is available to all undergraduate students at Berkeley with a declared major, with the exception of EECS majors. For information regarding minor requirements, please see the Minor Requirements tab on this page.
In addition to the University, campus, and college requirements, listed on the College Requirements tab, students must fulfill the below requirements specific to their major program.
General Guidelines
All courses taken to fulfill the major requirements below must be 3 or more units and taken for a letter grade.
All courses for the major must be technical in nature. Courses numbered 199, 198, 197, 196, 195, plus select 194, 191, 190, and various seminars do not count. If you are unsure, please check with the CS advisers (cs-advising@cs.berkeley.edu).
Only one upper division course may be used to simultaneously fulfill requirements for a student's major and minor programs. No more than two upper division courses can overlap between two majors.
A minimum grade point average (GPA) of 2.0 must be maintained in both upper and lower division courses used to fulfill the major requirements.
For information regarding residence requirements and unit requirements, please see the College Requirements tab.
Special Topics (15-Parallel Computing, 16-Introduction to Data Science [discontinued], 26-Computational Photography, 28-Computational Design and Fabrication, 30-Practical Networking, 31-Technology and Social Networks, 32-Computing for Social Good, 34-Cryptography [spring 2019], 35-Data Engineering, 73-Software Engineering for Scientific Computing, 80-Full Stack Deep Learning, 100-EECS for All: Social Justice in EECS, 126-PCP Design, 129-Designing, Visualizing and Understanding Deep Neural Networks [also fulfills design requirement], 131-Designing Technology to Counter Violent Extremism [Des Inv 190 does not count], 133-Collaborative Intelligent Agents and The DARPA Spectrum Challenge, 138-Cyberwar, 172-Computational Genomics, 177-Intro to Decentralized Finance, 196-Technology of Decentralization and Decentralized Intelligence, 198-Networks: Models, Processes & Algorithms, 224-Entrepreneurship in Web3, and 238-Special Topics in Zero Knowledge Proof.)
Technical electives: Effective Summer 2022, in addition to the 20 units of required CS coursework above, 4 units of technical electives must be taken from the list of approved non-computer science technical electives (see list below) for a total of 24 units for the major. See eecs.berkeley.edu/resources/undergrads/cs/degree-reqs/tech-electives for the most current list of approved technical electives. Students may not use CS upper division classes to fulfill the technical elective units. Contact cs-advising@cs.berkeley.edu with questions.
This program is geared toward students who would like to pursue an education beyond the BS/BA, allowing them to achieve greater breadth and/or depth of knowledge, and who would like to try their hand at research as well. It is not intended for students who have definitely decided to pursue a PhD immediately following graduation. Those students are advised to apply for a PhD program at Berkeley or elsewhere during their senior year. Students who have been accepted into the Five-Year BA/MS or BS/MS are free to change their minds later and apply to enter the PhD program or apply to a PhD program at another university. Note that admission is competitive with all our PhD applicants.
The program is focused on interdisciplinary training at a graduate level; with at least 8 units of course work outside EECS required. Students will emerge as leaders in their technical and professional fields.
Focused on interdisciplinary study and more experience in aligned technical fields such as physics, materials science, statistics, biology, etc., and/or professional disciplines such as management of technology, business, law, and public policy.
If admitted to the program, students must begin the graduate portion in the semester immediately following the conferral of the bachelor's degree.
Only one additional year (two semesters) beyond the bachelor's degree.
Only available to Berkeley EECS and CDSS CS undergraduates.
Participants in the program may serve as graduate student instructors (GSIs) with approval from their faculty research advisor and the Five-Year MS Committee.
Students who have a strong interest in an area of study outside their major often decide to complete a minor program. These programs have set requirements and are noted officially on the diploma and the awards section of the transcript.
General Guidelines
All minors must be declared before the first day of classes in your Expected Graduation Term (EGT). For summer graduates, minors must be declared prior to the first day of Summer Session A.
All upper-division courses must be taken for a letter grade.
A minimum of three of the upper-division courses taken to fulfill the minor requirements must be completed at UC Berkeley.
A minimum grade point average (GPA) of 2.0 is required in the upper-division courses to fulfill the minor requirements.
Courses used to fulfill the minor requirements may be applied toward the Seven-Course Breadth requirement, for Letters & Science students.
No more than one upper division course may be used to simultaneously fulfill requirements for a student's major and minor programs.
All minor requirements must be completed prior to the last day of finals during the semester in which the student plans to graduate. If students cannot finish all courses required for the minor by that time, they should see a College of Letters & Science adviser.
All minor requirements must be completed within the unit ceiling. (For further information regarding the unit ceiling, please see the College Requirements tab.)
The Computational Reasoning requirement is designed to provide a basic understanding of and competency in concepts such as programming, algorithms, iteration, and data-structures.
The Human and Social Dynamics of Data and Technology requirement is designed for the purpose of developing an understanding of how technology and data interact with human and societal contexts, including ethical considerations and applications such as education, health, law, natural resources, and public policy.
The Statistical Reasoning requirement is designed to provide basic understanding of and competency in the scientific approach to statistical problem solving, including uncertainty, prediction, and estimation.
The Reading and Composition requirement is the same as for the College of Letters and Science; it requires two semesters of lower division work in composition in sequence. Students must complete parts A & B reading and composition courses in sequential order by the end of their fourth semester.
The undergraduate breadth requirements are the same for CDSS students as for the College of Letters and Science, with the exception that a second semester foreign language course can be used to satisfy the International Studies breadth. To learn more about the L&S Seven-Course Breadth Requirement, visit the L&S Breadth Requirements page. To learn more about using a foreign language course to satisfy the International Studies breadth, visit the CDSS website page on Satisfying International Studies Breadth with a Foreign Language Course.
The undergraduate major programs in computer science, data science, and statistics have transitioned from the College of Letters & Science to CDSS. Students who were admitted in Spring 2024 or earlier have the option of completing either the L&S College Requirements, i.e., the breadth and essential skills requirements, or the CDSS college requirements (above).
All students must meet CDSS general policy (below). The one exception is with time-to-degree. Students admitted Fall 2022 or earlier are subject to the 130 unit maximum, rather than the 8 semester maximum (5 for transfer students).
Class Schedule Requirements
Minimum units per semester: 12
Maximum units per semester: 20.5
Academic (Grade) Requirements
Minimum cumulative GPA: 2.0
Minimum GPA for one semester: 1.5
Bachelor’s Degree Requirements
Minimum total units: 120. Of these 120 units:
PE maximum units: 4
Special Studies maximum units: 16
Maximum 300-499 course units: 6
Minimum upper division units: 36
Maximum number of semesters: 8 for first-year entrants; 5 for transfer students; summer terms do not count toward the maximum
Minimum GPA in upper division and graduate courses identified for the major: 2.0
Meet all major requirements
Meet all general, curricular, and residence requirements of the University of California and the Berkeley campus
For more information about CDSS requirements, visit student resources and information on the College of Computing, Data Science, and Society website.
All students who will enter the University of California as freshmen must demonstrate their command of the English language by satisfying the Entry Level Writing Requirement (ELWR). The UC Entry Level Writing Requirement website provides information on how to satisfy the requirement.
The American History and Institutions (AH&I) requirements are based on the principle that a US resident graduated from an American university should have an understanding of the history and governmental institutions of the United States.
The American Cultures requirement is a Berkeley campus requirement, one that all undergraduate students at Berkeley need to pass in order to graduate. You satisfy the requirement by passing, with a grade not lower than C- or P, an American Cultures course. You may take an American Cultures course any time during your undergraduate career at Berkeley. The requirement was instituted in 1991 to introduce students to the diverse cultures of the United States through a comparative framework. Courses are offered in more than fifty departments in many different disciplines at both the lower and upper division level.
Plan of Study (BA)
For more detailed information regarding the courses listed below (e.g., elective information, GPA requirements, etc.,), please see the Major Requirements tab.
Upper Division Elective major non-CS technical elective
4
Lower/Upper Division Elective
4
UD CDSS Elective
4
Lower/Upper Division Elective
3
UD CDSS Elective
3
15
11
Total Units: 113
Course Definitions
Upper Division major CS major course: course meeting the 20 units of upper division CS requirement (at least one of these must be a Design Course).
Upper Division major technical elective: course meeting the 4 units of non-CS technical electives requirement.
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NOTES
This is a sample program plan. This plan assumes that the student has completed the Entry Level Writing, American History and Institutions, Quantitative Reasoning, and Foreign Language requirements prior to admission.
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Students are strongly advised to work with an academic adviser to determine a personal program plan. Your program plan will differ depending on previous credit received, your course schedule, and available offerings.
Students are strongly advised to work with an academic adviser to determine a personal program plan. Your program plan will differ depending on previous credit received, your course schedule, and available offerings. Current students may make an appointment with a Computer Science Advisor in CalCentral; prospective students may make an appointment at https://berkeleycs.youcanbook.me/.
Accelerated Program Plans
For students considering graduating in less than four years, it's important to acknowledge the reasons to undertake such a plan of study. While there are advantages to pursuing a three-year degree plan such as reducing financial burdens, they are not for everyone and do involve sacrifices; especially with respect to participating in co-curricular activities, depth of study, and summer internships, which typically lead to jobs upon graduation. All things considered, please see the tables for three and three and a half year degree options.
Preparing graduates to pursue postgraduate education in electrical engineering, computer science, or related fields.
Preparing graduates for success in technical careers related to electrical and computer engineering, or computer science and engineering.
Preparing graduates to become leaders in fields related to electrical and computer engineering or computer science and engineering.
Learning Goals for the Major
An ability to apply knowledge of mathematics, science, and engineering.
An ability to configure, apply test conditions, and evaluate outcomes of experimental systems.
An ability to design systems, components, or processes that conform to given specifications and cost constraints.
An ability to work cooperatively, respectfully, creatively, and responsibly as a member of a team.
An ability to identify, formulate, and solve engineering problems.
An understanding of the norms of expected behavior in engineering practice and their underlying ethical foundations.
An ability to communicate effectively by oral, written, and graphical means.
An awareness of global and societal concerns and their importance in developing engineering solutions.
An ability to independently acquire and apply required information, and an appreciation of the associated process of lifelong learning.
A knowledge of contemporary issues.
An in-depth ability to use a combination of software, instrumentation, and experimental techniques practiced in circuits, physical electronics, communication, networks and systems, hardware, programming, and computer science theory.
Major Map
Major maps are experience maps that help undergraduates plan their Berkeley journey based on intended major or field of interest. Featuring student opportunities and resources from your college and department as well as across campus, each map includes curated suggestions for planning your studies, engaging outside the classroom, and pursuing your career goals in a timeline format.
Use the major map below to explore potential paths and design your own unique undergraduate experience:
Terms offered: Spring 2025, Fall 2024, Summer 2024 8 Week Session
This course offers an introduction to signals, systems, optimization, controls, and machine learning, all grounded in linear algebraic techniques. After a brief review of linear algebra, students will delve into topics such as signal processing, linear systems, feedback control, optimization methods, and foundational machine learning algorithms. Emphasizing practical applications, the course prepares EECS majors for advanced study by connecting mathematical concepts to real-world engineering problems. Foundations of Signals, Dynamical Systems, and Information Processing: Read More [+]
Terms offered: Spring 2025, Fall 2024, Spring 2024
This course teaches the fundamentals needed to predict the behavior of real-world electronic phenomena and applications via mathematical models and circuit analytical methods that simplify initially complex problems, rendering them solvable and understandable. Among the specific topics to be covered are time and frequency domain representation, complex arithmetic, phasor analysis of systems governed by differential equations, Kirchhoff’s laws, physical examples of electronic elements and devices, RLC circuits, op amp-based signal processors, feedback methods, and circuit models for domains beyond the electronic. Class contact time includes lectures, discussions, and a laboratory component designed to help solidify learned concepts. Introduction to Circuits & Devices: Read More [+]
Terms offered: Fall 2021
This course allows students who have had a linear algebra and/or basic circuit theory course to complete the work in EE16A and be ready for EE16B or EE47E. The course focuses on the fundamentals of designing modern information devices and systems that interface with the real world and provides a comprehensive foundation for core EECS topics in signal processing, learning, control, and circuit design. Modeling is emphasized in a way that deepens mathematical maturity, and in both labs and homework, students will engage computationally, physically, and visually with the concepts being introduced in addition to traditional paper/pencil exercises. Completion of work in Electrical Engineering 16A: Read More [+]
Rules & Requirements
Prerequisites:MATH 51; MATH 52; COMPSCI 61A (encouraged to be taken concurrently); college level courses in linear algebra and/or circuit theory; and consent of the instructor
Hours & Format
Fall and/or spring: 15 weeks - 2-8 hours of self-paced per week
Summer: 8 weeks - 4-13 hours of self-paced per week
Additional Details
Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Not yet offered
This course allows students who have had a linear algebra and/or basic circuit theory course to complete the work in EE16B. The course focuses on the fundamentals of designing modern information devices and systems that interface with the real world and provides a comprehensive foundation for core EECS topics in signal processing (DFT), learning (SVD/PCA), feedback control, and circuit design. Modeling is emphasized in a way that deepens mathematical maturity, and in both labs and homework, students will engage computationally, physically, and visually with the concepts being introduced in addition to traditional paper/pencil exercises. Completion of work in Electrical Engineering 16B: Read More [+]
Terms offered: Prior to 2007
This course allows students who have had a discrete math and/or probability course to complete the work in CS70. Logic, infinity, and induction; applications include undecidability and stable marriage problem. Modular arithmetic and GCDs; applications include primality testing and cryptography. Polynomials; examples include error correcting codes and interpolation. Probability including sample spaces, independence, random variables, law of large numbers; examples include load balancing, existence arguments, Bayesian inference. Completion of work in Computer Science 70: Read More [+]
Rules & Requirements
Prerequisites: Sophomore mathematical maturity, programming experience equivalent to that gained in COMPSCI 61A, a prior college level course on discrete math and/or probability, and consent of the instructor
Hours & Format
Fall and/or spring: 15 weeks - 3-8 hours of self-paced per week
Summer: 8 weeks - 6-16 hours of self-paced per week
Additional Details
Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
Terms offered: Fall 2024, Fall 2023, Fall 2022, Fall 2021, Fall 2020, Fall 2019
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 the level of EECS 16A/EECS 16B or MATH 54. Experience coding in python at the level of COMPSCI 61A. Preferred: experience developing software at the level of COMPSCI 61B and experience using Linux
Credit Restrictions: Students will receive no credit for Electrical Engineering and Computer Science C106A/Bioengineering C106A after completing EE C106A/BioE C125, Electrical Engineering 206A, or Electrical Engineering and Computer Science 206A.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week
Summer: 8 weeks - 6 hours of lecture, 2 hours of discussion, and 6 hours of laboratory per week
Additional Details
Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Spring 2025, Spring 2024, Spring 2023, Spring 2021, Spring 2020, Spring 2019
The course is a sequel to EECS/BIOE/MEC106A/EECSC206A, which covers the mathematical fundamentals of robotics including kinematics, dynamics and control as well as an introduction to path planning, obstacle avoidance, and computer vision. This course will present several areas of robotics and active vision, at a deeper level and informed by current research. Concepts will include the review at an advanced level of robot control, the kinematics, dynamics and control of multi-fingered hands, grasping and manipulation of objects, mobile robots: including non-holonomic motion planning and control, path planning, Simultaneous Localization And Mapping (SLAM), and active vision. Additional research topics covered at the instructor's discretion. Robotic Manipulation and Interaction: Read More [+]
Rules & Requirements
Prerequisites:EECS C106A / BIO ENG C106A / MEC ENG C106A / EECS C206A or an equivalent course. A strong programming background, knowledge of Python and Matlab, and some coursework in feedback controls (such as EL ENG C128 / MEC ENG C134) are also useful. Students who have not taken the prerequisite course should have a strong programming background, knowledge of Python and Matlab, and exposure to linear algebra, Lagrangian dynamics, and feedback controls at the intermediate level. EECS C106A
Credit Restrictions: Students will receive no credit for Electrical Engineering and Computer Science C106B/Bioengineering C106B after completing Electrical Engineering C106B/Bioengineering C125B, Electrical Engineering 206B, or Electrical Engineering and Computer Science 206B.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week
Additional Details
Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate
Grading/Final exam status: Letter grade. Alternative to final exam.
Terms offered: Not yet offered
This course develops a fundamental understanding of computer
vision (CV) and computer graphics (CG) that underpin the
emerging AR/VR and Metaverse applications. The syllabus
includes 3D perception, near-eye optics, depth cameras, 3D
localization, and immersive 3D user experience. The companion
lab helps students to acquire basic AR/VR coding skills in Unity
and develop Metaverse applications. The course builds a strong
foundation for students to take more advanced course: CS
294-137. Introduction to AR/VR and Applications in Metaverse: Read More [+]
Rules & Requirements
Prerequisites: Familiarity with Linear Algebra at the level of EECS 16A/B or MATH 54. Experience on coding proficiency and data structures at the level of CS61 A/B
Repeat rules: Course may be repeated for credit with instructor consent.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week
Additional Details
Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate
Grading/Final exam status: Letter grade. Alternate method of final assessment during regularly scheduled final exam group (e.g., presentation, final project, etc.).
Terms offered: Spring 2025, Fall 2024, Spring 2024
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains. Probability and Random Processes: Read More [+]
Rules & Requirements
Prerequisites:COMPSCI 70 preferred but not required; Familiarity with linear algebra
Credit Restrictions: Students will receive no credit for EECS 126 after completing EE 126.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
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 [+]
Terms offered: Fall 2024, Fall 2023, Fall 2022
This course introduces students to the basics of modeling, analysis, and design of embedded, cyber-physical systems. Students learn how to integrate computation with physical processes to meet a desired specification. Topics include models of computation, control, analysis and verification, interfacing with the physical world, real-time behaviors, 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 and Cyber Physical Systems: Read More [+]
Objectives & Outcomes
Course Objectives: To develop the skills to realize embedded systems that are safe, reliable, and efficient in their use of resources. To learn how to model and design the joint dynamics of software, networks, and physical processes. To learn to think critically about technologies that are available for achieving such joint dynamics.
Terms offered: Spring 2025, Fall 2024, Spring 2024
An introduction to digital 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.
The class has two lab options: ASIC Lab (EECS 151LA) and FPGA Lab (EECS 151LB). Students must enroll in at least one of the labs concurrently with the class. 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.
Credit Restrictions: Students must enroll concurrently in at least one the lab flavors EECS151LA or EECS151LB. Students wishing to take a second lab flavor next term can sign-up only for that Lab section and receive a Letter grade. The pre-requisite for “Lab-only” enrollment that term will be EECS151 from previous terms.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Additional Details
Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate
Grading/Final exam status: Letter grade. Final exam required.
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 three-stage RISC-V processor with a register file and caches. The design is mapped to simulation and layout specification. Application Specific Integrated Circuits Laboratory: Read More [+]
Objectives & Outcomes
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.
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 three-stage RISC-V processor system, with caches, graphics acceleration, and external peripheral components. The design is mapped and demonstrated on an FPGA hardware platform. Field-Programmable Gate Array Laboratory: Read More [+]
Terms offered: Spring 2025, Fall 2024, Summer 2024 8 Week Session, Fall 2023, Spring 2023, Fall 2022, Spring 2022, Fall 2021, Summer 2021 8 Week Session, Fall 2020
Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership. Foundations of Data Science: Read More [+]
Rules & Requirements
Prerequisites: This course may be taken on its own, but students are encouraged to take it concurrently with a data science connector course (numbered 88 in a range of departments)
Terms offered: Spring 2025, Fall 2024, Summer 2024 8 Week Session
An introductory course for students with minimal prior exposure to computer science. Prepares students for future computer science courses and empowers them to utilize programming to solve problems in their field of study. Presents an overview of the history, great principles, and transformative applications of computer science, as well as a comprehensive introduction to programming. Topics include abstraction, recursion, algorithmic complexity, higher-order functions, concurrency, social implications of computing (privacy, education, algorithmic bias), and engaging research areas (data science, AI, HCI). Students will program in Snap! (a friendly graphical language) and Python, and will design and implement two projects of their choice. The Beauty and Joy of Computing: Read More [+]
Rules & Requirements
Credit Restrictions: Students will receive no credit for 10 after having taken W10, 61A, 61B, or 61C.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture, 1 hour of discussion, and 4 hours of laboratory per week
Summer: 8 weeks - 4 hours of lecture, 2 hours of discussion, and 8 hours of laboratory per week
Terms offered: Fall 2012
This course meets the programming prerequisite for 61A. An introduction to the beauty and joy of computing. The history, social implications, great principles, and future of computing. Beautiful applications that have changed the world. How computing empowers discovery and progress in other fields. Relevance of computing to the student and society will be emphasized. Students will learn the joy of programming a computer using a friendly, graphical language, and will complete a substantial team programming project related to their interests. The Beauty and Joy of Computing: Read More [+]
Rules & Requirements
Credit Restrictions: Students will receive no credit for W10 after taking 10, 61A, 61B or 61C. A deficient grade in 10 may be removed by taking W10.
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of web-based lecture and 5 hours of web-based discussion per week
Summer: 8 weeks - 4 hours of web-based lecture and 10 hours of web-based discussion per week
Terms offered: Fall 2019, Fall 2018, Spring 2018
Computer Science 36 is a seminar for CS Scholars who are concurrently taking CS61A: The Structure and Interpretation of Computer Programs. CS Scholars is a cohort-model program to provide support in exploring and potentially declaring a CS major for students with little to no computational background prior to coming to the university. CS 36 provides an introduction to the CS curriculum at UC Berkeley, and the overall CS landscape in both industry and academia—through the lens of accessibility and its relevance to diversity. Additionally, CS36 provides technical instruction to review concepts in CS61A, in order to support CS Scholars’ individual learning and success in the CS61A course. CS Scholars Seminar: The Educational Climate in CS & CS61A technical discussions: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Students will know where to find several support services including tutoring, advising, counseling, and career advice.
Students will perform as well as possible in the CS61A prerequisite for the CS major. They will also have customized program plans for completing the major within four years.
Rules & Requirements
Prerequisites: Prerequisite satisfied Concurrently: Participating in the CS Scholars program, and concurrently taking COMPSCI 61A
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of seminar per week
Terms offered: Spring 2025, Fall 2023, Spring 2022
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25. Freshman/Sophomore Seminar: Read More [+]
Rules & Requirements
Prerequisites: Priority given to freshmen and sophomores
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 15 weeks - 2-3 hours of seminar per week
Terms offered: Spring 2025, Fall 2024, Spring 2024
Implementation of generic operations. Streams and iterators. Implementation techniques for supporting functional, object-oriented, and constraint-based programming in the Scheme programming language. Together with 9D, 47A constitutes an abbreviated, self-paced version of 61A for students who have already taken a course equivalent to 61B. Completion of Work in Computer Science 61A: Read More [+]
Rules & Requirements
Prerequisites:COMPSCI 61B, COMPSCI 9D, and consent of instructor
Credit Restrictions: Students will receive no credit for 47A after taking 61A.
Hours & Format
Fall and/or spring: 15 weeks - 0 hours of self-paced per week
Terms offered: Spring 2025, Fall 2024, Spring 2024
Iterators. Hashing, applied to strings and multi-dimensional structures. Heaps. Storage management. Design and implementation of a program containing hundreds of lines of code. Students who have completed a portion of the subject matter of COMPSCI 61B may, with consent of instructor, complete COMPSCI 61B in this self-paced course. Please note that students in the College of Engineering are required to receive additional permission from the College as well as the EECS department for the course to count in place of COMPSCI 61B. Completion of Work in Computer Science 61B: Read More [+]
Rules & Requirements
Prerequisites: A course in data structures, COMPSCI 9G, and consent of instructor
Credit Restrictions: Students will receive no credit for 47B after taking 61B.
Hours & Format
Fall and/or spring: 15 weeks - 0 hours of self-paced per week
Terms offered: Spring 2025, Fall 2024, Spring 2024
MIPS instruction set simulation. The assembly and linking process. Caches and virtual memory. Pipelined computer organization. Students with sufficient partial credit in 61C may, with consent of instructor, complete the credit in this self-paced course. Completion of Work in Computer Science 61C: Read More [+]
Rules & Requirements
Prerequisites: Experience with assembly language including writing an interrupt handler, COMPSCI 9C, and consent of instructor
Credit Restrictions: Students will receive no credit for COMPSCI 47C after completing COMPSCI 61C, or COMPSCI 61CL.
Hours & Format
Fall and/or spring: 15 weeks - 0 hours of self-paced per week
Terms offered: Spring 2025, Fall 2024, Summer 2024 8 Week Session
An introduction to programming and computer science focused on abstraction techniques as means to manage program complexity. Techniques include procedural abstraction; control abstraction using recursion, higher-order functions, generators, and streams; data abstraction using interfaces, objects, classes, and generic operators; and language abstraction using interpreters and macros. The course exposes students to programming paradigms, including functional, object-oriented, and declarative approaches. It includes an introduction to asymptotic analysis of algorithms. There are several significant programming projects. The Structure and Interpretation of Computer Programs: Read More [+]
Rules & Requirements
Prerequisites:MATH 51 (may be taken concurrently); or MATH 10A; or MATH 16A; and programming experience equivalent to that gained from a score of 3 or above on the Advanced Placement Computer Science exam
Terms offered: Spring 2025, Fall 2024, Spring 2024
Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays strings, and hash tables. Storage management. Elementary principles of software engineering. Abstract data types. Algorithms for sorting and searching. Introduction to the Java programming language. Data Structures: Read More [+]
Terms offered: Spring 2025, Fall 2024, Summer 2024 8 Week Session
The internal organization and operation of digital computers. Machine architecture, support for high-level languages (logic, arithmetic, instruction sequencing) and operating systems (I/O, interrupts, memory management, process switching). Elements of computer logic design. Tradeoffs involved in fundamental architectural design decisions. Great Ideas of Computer Architecture (Machine Structures): Read More [+]
Rules & Requirements
Prerequisites:COMPSCI 61A, along with either COMPSCI 61B or COMPSCI 61BL, or programming experience equivalent to that gained in COMPSCI 9C, COMPSCI 9F, or COMPSCI 9G
Credit Restrictions: Students will receive no credit for COMPSCI 61C after completing COMPSCI 61CL.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 2 hours of laboratory per week
Summer: 8 weeks - 6 hours of lecture, 2 hours of discussion, and 4 hours of laboratory per week
Terms offered: Summer 2019 8 Week Session
An introduction to programming and computer science focused on abstraction techniques as means to manage program complexity. Techniques include procedural abstraction; control abstraction using recursion, higher-order functions, generators, and streams; data abstraction using interfaces, objects, classes, and generic operators; and language abstraction using interpreters and macros. The course exposes students to programming paradigms, including functional, object-oriented, and declarative approaches. It includes an introduction to asymptotic analysis of algorithms. There are several significant programming projects. The Structure and Interpretation of Computer Programs (Online): Read More [+]
Rules & Requirements
Prerequisites: MATH 1A (may be taken concurrently); programming experience equivalent to that gained from a score of 3 or above on the Advanced Placement Computer Science A exam
Credit Restrictions: Students will receive no credit for Computer Science W61A after completing Computer Science 47A or Computer Science 61A. A deficient grade in Computer Science W61A may be removed by taking Computer Science 61A.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of web-based lecture, 1.5 hours of laboratory, and 1.5 hours of web-based discussion per week
Summer: 8 weeks - 6 hours of web-based lecture, 3 hours of laboratory, and 3 hours of web-based discussion per week
Terms offered: Spring 2025, Fall 2024, Summer 2024 8 Week Session
Logic, infinity, and induction; applications include undecidability and stable marriage problem. Modular arithmetic and GCDs; applications include primality testing and cryptography. Polynomials; examples include error correcting codes and interpolation. Probability including sample spaces, independence, random variables, law of large numbers; examples include load balancing, existence arguments, Bayesian inference. Discrete Mathematics and Probability Theory: Read More [+]
Rules & Requirements
Prerequisites: Sophomore mathematical maturity, and programming experience equivalent to that gained with a score of 3 or above on the Advanced Placement Computer Science A exam
Credit Restrictions: Students will receive no credit for Computer Science 70 after taking Mathematics 55.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week
Summer: 8 weeks - 6 hours of lecture and 4 hours of discussion per week
Terms offered: Fall 2007
Sophomore seminars are small interactive courses offered by faculty members in departments all across the campus. Sophomore seminars offer opportunity for close, regular intellectual contact between faculty members and students in the crucial second year. The topics vary from department to department and semester to semester. Enrollment limited to 15 sophomores. Sophomore Seminar: Read More [+]
Rules & Requirements
Prerequisites: At discretion of instructor
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 5 weeks - 3-6 hours of seminar per week 10 weeks - 1.5-3 hours of seminar per week 15 weeks - 1-2 hours of seminar per week
Summer: 6 weeks - 2.5-5 hours of seminar per week 8 weeks - 2-4 hours of seminar per week
Terms offered: Spring 2025, Fall 2024, Summer 2024 8 Week Session, Spring 2023, Fall 2022
Development of Computer Science topics appearing in Foundations of Data Science (C8); expands computational concepts and techniques of abstraction. Understanding the structures that underlie the programs, algorithms, and languages used in data science and elsewhere. Mastery of a particular programming language while studying general techniques for managing program complexity, e.g., functional, object-oriented, and declarative programming. Provides practical experience with composing larger systems through several significant programming projects. Computational Structures in Data Science: Read More [+]
Objectives & Outcomes
Course Objectives: Develop a foundation of computer science concepts that arise in the context of data analytics, including algorithm, representation, interpretation, abstraction, sequencing, conditional, function, iteration, recursion, types, objects, and testing, and develop proficiency in the application of these concepts in the context of a modern programming language at a scale of whole programs on par with a traditional CS introduction course.
Student Learning Outcomes: Students will be able to demonstrate a working knowledge of these concepts and a proficiency of programming based upon them sufficient to construct substantial stand-alone programs.
Rules & Requirements
Credit Restrictions: Students will receive no credit for DATA C88C after completing COMPSCI 61A.
Hours & Format
Fall and/or spring: 15 weeks - 2-2 hours of lecture, 2-2 hours of laboratory, and 0-1 hours of supplement per week
Summer: 8 weeks - 4-4 hours of lecture, 4-4 hours of laboratory, and 0-2 hours of supplement per week
Terms offered: Spring 2025, Fall 2024, Summer 2024 8 Week Session, Spring 2024, Fall 2022, Fall 2021, Fall 2020
In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. This class will focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing. Principles & Techniques of Data Science: Read More [+]
Rules & Requirements
Prerequisites:DATA C8 or STAT 20 with a C- or better, or Pass; and COMPSCI 61A, COMPSCI/DATA C88C, or ENGIN 7 with a C- or better, or Pass; Corequisite: MATH 54, 56, 110, EECS 16A, PHYSICS 89 or equivalent linear algebra (C- or better, or Pass, required if completed prior to Data C100)
Terms offered: Spring 2025, Spring 2024, Spring 2023
Instruction set architecture, microcoding, pipelining (simple and complex). Memory hierarchies and virtual memory. Processor parallelism: VLIW, vectors, multithreading. Multiprocessors. Computer Architecture and Engineering: Read More [+]
Terms offered: Spring 2025, Summer 2024 8 Week Session, Spring 2024
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 [+]
Terms offered: Spring 2025, Fall 2024, Summer 2024 8 Week Session
Introduction to computer security. Cryptography, including encryption, authentication, hash functions, cryptographic protocols, and applications. Operating system security, access control. Network security, firewalls, viruses, and worms. Software security, defensive programming, and language-based security. Case studies from real-world systems. Computer Security: Read More [+]
Terms offered: Spring 2025, Fall 2024, Spring 2024
Basic concepts of operating systems and system programming. Utility programs, subsystems, multiple-program systems. Processes, interprocess communication, and synchronization. Memory allocation, segmentation, paging. Loading and linking, libraries. Resource allocation, scheduling, performance evaluation. File systems, storage devices, I/O systems. Protection, security, and privacy. Operating Systems and System Programming: Read More [+]
Terms offered: Spring 2025, Fall 2024, Spring 2024
Survey of programming languages. The design of modern programming languages. Principles and techniques of scanning, parsing, semantic analysis, and code generation. Implementation of compilers, interpreters, and assemblers. Overview of run-time organization and error handling. Programming Languages and Compilers: Read More [+]
Terms offered: Spring 2025, Fall 2024, Spring 2024
This course is an introduction to the Internet architecture. We will focus on the concepts and fundamental design principles that have contributed to the Internet's scalability and robustness and survey the various protocols and algorithms used within this architecture. Topics include layering, addressing, intradomain routing, interdomain routing, reliable delivery, congestion control, and the core protocols (e.g., TCP, UDP, IP, DNS, and HTTP) and network technologies (e.g., Ethernet, wireless). Introduction to the Internet: Architecture and Protocols: Read More [+]
Terms offered: Fall 2024, Summer 2024 8 Week Session, Fall 2023
Ideas and techniques for designing, developing, and modifying large software systems. Service-oriented architecture, behavior-driven design with user stories, cloud computing, test-driven development, automated testing, cost and quality metrics for maintainability and effort estimation, practical performance and security in software operations, design patterns and refactoring, specification and documentation, agile project team organization and management. Introduction to Software Engineering: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Students will learn how to approach and add functionality to a legacy code base;
Students will learn how to identify, measure, and resolve maintainability problems in code;
Students will learn how to work with nontechnical customers and convert customer requirements into a software plan that can be effort-estimated, built, and deployed to the public cloud, including the use of behavior-driven design, user stories, and velocity;
Students will learn how to write automated tests and measure test coverage;
Students will learn practical security and performance considerations for SaaS applications.
Students will learn the architecture and machinery of software as a service; the agile/XP methodology for software development and how it compares with other methodologies, including "Plan-and-document" methodologies;
Students will learn the role of software design patterns in refactoring, and how to identify opportunities to use them;
Credit Restrictions: Students will receive no credit for COMPSCI 169A after completing COMPSCI 169, or COMPSCI W169A. A deficient grade in COMPSCI 169A may be removed by taking COMPSCI 169, or COMPSCI W169A.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week
Terms offered: Spring 2025, Spring 2024, Spring 2023
Open-ended design project enhancing or creating software for real customers in an agile team setting. Teamwork coordination, effective customer meetings, pre- and post-iteration team meetings, running scrums and standups, technical communication. Contributing as a team to an open-source project; tools and workflows associated with open source collaboration, including fork-and-pull, rebase, upstream merge, continuous deployment & integration. Software Engineering Team Project: Read More [+]
Objectives & Outcomes
Course Objectives: Students will work in a team to develop new software or enhance existing software for a customer with a real business need.
Student Learning Outcomes: Students will learn how to conduct effective meetings with nontechnical customers and work with their feedback;
Students will learn how to coordinate teamwork on developing, testing, and deploying features; and in most cases, how to approach a legacy codebase and add features to it.
Students will learn to run a small team including rotation of team roles such as product owner, scrum master, and so on;
Terms offered: Summer 2021 8 Week Session, Fall 2020, Summer 2020 8 Week Session
This course presents ideas and techniques for designing, developing, and modifying large software systems using Agile techniques and tools. Topics include: function-oriented and object-oriented modular design techniques, designing for re-use and maintainability including proper use of design patterns, behavior-driven design, test-driven development, user stories for requirements elicitation & documentation, verification and validation, cost and quality metrics and estimation, project team organization and management, analyzing and refactoring legacy code. Software Engineering: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Students will learn how to apply BDD & TDD to identify the main parts of a legacy code base, measure code quality, and refactor code to improve its quality;
Students will learn how to apply behavior-driven development (BDD) to elicit customer needs and express them as user stories that will drive development;
Students will learn how to apply the key ideas of learning a new framework to construct and deploy simple Rails applications;
Students will learn how to apply the key ideas of learning a new language in order to construct programs in Ruby;
Students will learn how to construct unit- and module-level tests and measure their coverage;
Students will learn how to exercise best practices in planning, effort estimation, and coordination of the efforts of small software teams, using appropriate tools to support those practices;
Students will learn how to identify and repair potential app-level security and performance problems.
Students will learn how to recognize when an appropriate Design Pattern may improve code quality, and refactor code to apply those Design Patterns;
Students will learn how to summarize the key architectural elements of RESTful SaaS applications and microservices;
Students will learn to articulate the primary differences between Agile and Plan-and-Document methodologies;
Credit Restrictions: Students will receive no credit for COMPSCI W169A after completing COMPSCI 169, or COMPSCI 169A. A deficient grade in COMPSCI W169A may be removed by taking COMPSCI 169, or COMPSCI 169A.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 1 hour of discussion per week
Summer: 8 weeks - 6 hours of web-based lecture and 0 hours of discussion per week
Terms offered: Spring 2025, Fall 2024, Spring 2024
Concept and basic techniques in the design and analysis of algorithms; models of computation; lower bounds; algorithms for optimum search trees, balanced trees and UNION-FIND algorithms; numerical and algebraic algorithms; combinatorial algorithms. Turing machines, how to count steps, deterministic and nondeterministic Turing machines, NP-completeness. Unsolvable and intractable problems. Efficient Algorithms and Intractable Problems: Read More [+]
Terms offered: Spring 2024, Spring 2021
Cryptography or cryptology is the science of designing algorithms and protocols for enabling parties to communicate and compute securely in an untrusted environment (e.g. secure communication, digital signature, etc.) Over the last four decades, cryptography has transformed from an ad hoc collection of mysterious tricks into a rigorous science based on firm complexity-theoretic foundations. This modern complexity-theoretic approach to cryptography will be the focus. E.g., in the context of encryption we will begin by giving a precise mathematical definition for what it means to be a secure encryption scheme and then give a construction (realizing this security notion) assuming various computational hardness assumptions (e.g. factoring). Cryptography: Read More [+]
Terms offered: Fall 2024, Fall 2022, Spring 2022
Finite automata, Turing machines and RAMs. Undecidable, exponential, and polynomial-time problems. Polynomial-time equivalence of all reasonable models of computation. Nondeterministic Turing machines. Theory of NP-completeness: Cook's theorem, NP-completeness of basic problems. Selected topics in language theory, complexity and randomness. Computability and Complexity: Read More [+]
Terms offered: Spring 2025, Spring 2023, Spring 2022
Permutations, combinations, principle of inclusion and exclusion, generating functions, Ramsey theory. Expectation and variance, Chebychev's inequality, Chernov bounds. Birthday paradox, coupon collector's problem, Markov chains and entropy computations, universal hashing, random number generation, random graphs and probabilistic existence bounds. Combinatorics and Discrete Probability: Read More [+]
Terms offered: Fall 2020, Fall 2018, Fall 2017
Algorithms and probabilistic models that arise in various computational biology applications: suffix trees, suffix arrays, pattern matching, repeat finding, sequence alignment, phylogenetics, genome rearrangements, hidden Markov models, gene finding, motif finding, stochastic context free grammars, RNA secondary structure. There are no biology prerequisites for this course, but a strong quantitative background will be essential. Algorithms for Computational Biology: Read More [+]
Rules & Requirements
Prerequisites:COMPSCI 70 and COMPSCI 170; experience programming in a language such as C, C++, Java, or Python
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Terms offered: Spring 2025, Fall 2022
This course will provide familiarity with algorithms and probabilistic models that arise in various computational biology applications, such as suffix trees, suffix arrays, pattern matching, repeat finding, sequence alignment, phylogenetics, hidden Markov models, gene finding, motif finding, linear/logistic regression, random forests, convolutional neural networks, genome-wide association studies, pathogenicity prediction, and sequence-to-epigenome prediction. Algorithms for Computational Biology: Read More [+]
Objectives & Outcomes
Student Learning Outcomes: Understand the basic elements of molecular, cell, and evolutionary biology.
Understand the key probabilistic and machine learning models used in computational biology applications.
Understand various data structures and algorithms that arise in computational biology.
Terms offered: Spring 2025, Spring 2024
The class provides an introduction to algorithmic questions in economic design. The class will cover problems of public goods and social choice, as well as allocative questions and private consumption. The focus is on normative questions: From the perspective of social goals, these are efficiency, fairness, and equity. In terms of private goals, the focus is on revenue maximization. The course will cover voting, fair division, pricing and market mechanisms. There is an emphasis on the algorithmic questions that arise naturally in economic design. Algorithmic Economics: Read More [+]
Rules & Requirements
Prerequisites: Students should be comfortable with formal mathematical proofs, and will be expected to write proofs on their own
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Terms offered: Fall 2024, Fall 2023
This advanced undergraduate 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 [+]
Objectives & Outcomes
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-from-motion, multi-view stereo, and the plenoptic function. Students will learn the fundamentals of image processing from the 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.
Terms offered: Spring 2025, Fall 2024, Spring 2008, Spring 2007
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 [+]
Objectives & Outcomes
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.
Credit Restrictions: Students will receive no credit for COMPSCI 182 after completing COMPSCI W182, or COMPSCI L182. A deficient grade in COMPSCI 182 may be removed by taking COMPSCI L182, COMPSCI W182, COMPSCI W182, or COMPSCI L182.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
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. Algorithms for clipping, hidden surface removal, rasterization, and anti-aliasing. Scan-line based and ray-based rendering algorithms. Lighting models for reflection, refraction, transparency. Foundations of Computer Graphics: Read More [+]
Rules & Requirements
Prerequisites:COMPSCI 61B or COMPSCI 61BL; programming skills in C, C++, or Java; linear algebra and calculus
Credit Restrictions: Students will receive no credit for Comp Sci 184 after taking Comp Sci 284A.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week
Terms offered: Not yet offered
This course will cover the intersection of control, reinforcement learning, and deep learning. This course will provide an 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, including exploration, model-based learning with video prediction, transfer learning, multi-task learning, and meta-learning. Homework assignments will cover imitation learning, policy gradients, Q-learning, and model-based reinforcement learning, as well as a final project. Deep Reinforcement Learning, Decision Making, and Control: Read More [+]
Rules & Requirements
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)
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Terms offered: Spring 2025, Fall 2024, Spring 2024
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 [+]
Terms offered: Fall 2021, Spring 2021, Spring 2020
Broad introduction to systems for storing, querying, updating and managing large databases. Computer science skills synthesizing viewpoints from low-level systems architecture to high-level modeling and declarative logic. System internals, including the complex details of query optimization and execution, concurrency control, indexing, and memory management. More abstract issues in query languages and data modeling – students are exposed to formal relational languages, SQL, full-text search, entity-relationship modeling, normalization, and physical database design. Recent technological trends in the field, including “Big Data” programming libraries like MapReduce, and distributed key-value stores with various consistency models. Introduction to Database Systems: Read More [+]
Terms offered: Spring 2025
This course will cover the principles and practices of managing data at scale, with a focus on use cases in data analysis and machine learning. We will cover the entire life cycle of data management and science, ranging from data preparation to exploration, visualization and analysis, to machine learning and collaboration, with a focus on ensuring reliable, scalable operationalization.
Terms offered: Spring 2025, Fall 2024, Summer 2024 8 Week Session
Ideas and techniques underlying the design of intelligent computer systems. Topics include search, game playing, knowledge representation, inference, planning, reasoning under uncertainty, machine learning, robotics, perception, and language understanding. Introduction to Artificial Intelligence: Read More [+]
Terms offered: Spring 2025, Fall 2024, Spring 2024
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications. Introduction to Machine Learning: Read More [+]
Terms offered: Spring 2025, Spring 2024, Fall 2023
This multidisciplinary course provides an introduction to fundamental conceptual aspects of quantum mechanics from a computational and informational theoretic perspective, as well as physical implementations and technological applications of quantum information science. Basic sections of quantum algorithms, complexity, and cryptography, will be touched upon, as well as pertinent physical realizations from nanoscale science and engineering. Introduction to Quantum Computing: Read More [+]
Terms offered: Spring 2025, Fall 2024, Spring 2024
Topics will vary semester to semester. See the Computer Science Division announcements. Special Topics: Read More [+]
Rules & Requirements
Prerequisites: Consent of instructor
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of lecture per week
Terms offered: Spring 2025, Fall 2024, Fall 2023
Topics include electronic community; the changing nature of work; technological risks; the information economy; intellectual property; privacy; artificial intelligence and the sense of self; pornography and censorship; professional ethics. Students will lead discussions on additional topics. Social Implications of Computer Technology: Read More [+]
Rules & Requirements
Credit Restrictions: Students will receive no credit for 195 after taking C195/Interdisciplinary Field Study C155 or H195.
Hours & Format
Fall and/or spring: 15 weeks - 1.5 hours of lecture per week
Terms offered: Spring 2025, Fall 2024, Fall 2023
Topics include electronic community; the changing nature of work; technological risks; the information economy; intellectual property; privacy; artificial intelligence and the sense of self; pornography and censorship; professional ethics. Students may lead discussions on additional topics. Honors Social Implications of Computer Technology: Read More [+]
Rules & Requirements
Credit Restrictions: Student will receive no credit for H195 after taking 195 or C195.
Hours & Format
Fall and/or spring: 15 weeks - 1.5 hours of lecture and 1.5 hours of discussion per week
Terms offered: Fall 2021, Fall 2020, Fall 2016
Thesis work under the supervision of a faculty member. To obtain credit the student must, at the end of two semesters, submit a satisfactory thesis to the Electrical Engineering and Computer Science department archive. A total of four units must be taken. The units many be distributed between one or two semesters in any way. H196A-H196B count as graded technical elective units, but may not be used to satisfy the requirement for 27 upper division technical units in the College of Letters and Science with a major in Computer Science. Senior Honors Thesis Research: Read More [+]
Rules & Requirements
Prerequisites: Open only to students in the computer science honors program
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of independent study per week
Terms offered: Spring 2023, Spring 2010, Spring 2009
Thesis work under the supervision of a faculty member. To obtain credit the student must, at the end of two semesters, submit a satisfactory thesis to the Electrical Engineering and Computer Science department archive. A total of four units must be taken. The units many be distributed between one or two semesters in any way. H196A-H196B count as graded technical elective units, but may not be used to satisfy the requirement for 27 upper division technical units in the College of Letters and Science with a major in Computer Science. Senior Honors Thesis Research: Read More [+]
Rules & Requirements
Prerequisites: Open only to students in the computer science honors program
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of independent study per week
Terms offered: Fall 2023, Spring 2019, Fall 2018
Students take part in organized individual field sponsored programs with off-campus companies or tutoring/mentoring relevant to specific aspects and applications of computer science on or off campus. Note Summer CPT or OPT students: written report required. Course does not count toward major requirements, but will be counted in the cumulative units toward graduation. Field Study: Read More [+]
Rules & Requirements
Prerequisites: Consent of instructor (see department adviser)
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of fieldwork per week
Summer: 6 weeks - 2.5-10 hours of fieldwork per week 8 weeks - 2-7.5 hours of fieldwork per week
Terms offered: Fall 2022, Fall 2021, Fall 2017
The Freshman Seminar Program has been designed to provide new students with the opportunity to explore an intellectual topic with a faculty member in a small seminar setting. Freshman seminars are offered in all campus departments, and topics may vary from department to department and semester to semester. Freshman Seminar: Read More [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1 hour of seminar per week
Terms offered: Fall 2011
A Berkeley Electrical Engineering and Computer Sciences degree opens the door to many opportunities, but what exactly are they? Graduation is only a few years away and it's not too early to find out. In this seminar students will hear from practicing engineers who recently graduated. What are they working on? Are they working in a team? What do they wish they had learned better? How did they find their jobs? What Electrical Engineers Do--Feedback from Recent Graduates: Read More [+]
Hours & Format
Fall and/or spring: 15 weeks - 1 hour of lecture per week
Terms offered: Spring 2025, Spring 2023, Fall 2022
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25. Freshman/Sophomore Seminar: Read More [+]
Rules & Requirements
Prerequisites: Priority given to freshmen and sophomores
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 15 weeks - 2-4 hours of seminar per week
Terms offered: Fall 2013, Summer 2013 8 Week Session, Spring 2013
Introduction to the principles of electrical engineering, starting from the basic concepts of voltage and current and circuit elements of resistors, capacitors, and inductors. Kirchhoff's voltage and current laws with Thevenin and Norton equivalents. Operational amplifiers with feedback. Semiconductor devices including diodes and MOSFETS and their IV characteristics. Applications of diodes for rectification, and design of MOSFETs in common source amplifiers. Digital logic gates and design using CMOS as well as simple flip-flops. Speed and scaling issues for CMOS. The course includes as motivating examples designs of high level applications including logic circuits, amplifiers, power supplies, and communication links.
Terms offered: Spring 2020, Spring 2019, Fall 2018
Electronics has become pervasive in our lives as a powerful technology with applications in a wide range of fields including healthcare, environmental monitoring, robotics, or entertainment. This course teaches how to build electronic circuits that interact with the environment through sensors and actuators and how to communicate wirelessly with the internet to cooperate with other devices and with humans. In the laboratory students design and build representative samples such as solar harvesters, robots, that exchange information with or are controlled from the cloud. Electronics for the Internet of Things: Read More [+]
Objectives & Outcomes
Course Objectives: Electronics has become a powerful and ubiquitous technology supporting solutions to a wide range of applications in fields ranging from science, engineering, healthcare, environmental monitoring, transportation, to entertainment. The objective of this course is to teach students majoring in these and related subjects how to use electronic devices to solve problems in their areas of expertise. Through the lecture and laboratory, students gain insight into the possibilities and limitations of the technology and how to use electronics to help solve problems. Students learn to use electronics to interact with the environment through sound, light, temperature, motion using sensors and actuators, and how to use electronic computation to orchestrate the interactions and exchange information wirelessly over the internet.
Student Learning Outcomes: Deploy electronic sensors and interface them to microcontrollers through digital and analog channels as well as common protocols (I2C, SPI),
Design, build and test electronic devices leveraging these concepts.
Interact with the internet and cloud services using protocols such as http, MQTT, Blynk,
Interface DC motors, steppers and servos to microcontrollers,
Represent information with voltage, current, power, and energy and how to measure these quantities with laboratory equipment,
To use and program low-cost and low-power microcontrollers for sensing, actuation, and information processing, and find and use program libraries supporting these tasks
Understand and make basic low-pass and high-pass filters, Wheatstone bridge etc.
Use electronics to sense and actuate physical parameters such as temperature, humidity, sound, light, and motion,
Terms offered: Spring 2025, Fall 2022, Spring 2022
Sophomore seminars are small interactive courses offered by faculty members in departments all across the campus. Sophomore seminars offer opportunity for close, regular intellectual contact between faculty members and students in the crucial second year. The topics vary from department to department and semester to semester. Enrollment limited to 15 sophomores. Sophomore Seminar: Read More [+]
Rules & Requirements
Prerequisites: At discretion of instructor
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 5 weeks - 3-6 hours of seminar per week 10 weeks - 1.5-3 hours of seminar per week 15 weeks - 1-2 hours of seminar per week
Summer: 6 weeks - 2.5-5 hours of seminar per week 8 weeks - 1.5-3.5 hours of seminar per week
Terms offered: Summer 2024 3 Week Session, Spring 2016, Fall 2015
Students take part in organized individual field sponsored programs with off-campus companies or tutoring/mentoring relevant to specific aspects and applications of computer science on or off campus. Note Summer CPT or OPT students: written report required. Course does not count toward major requirements, but will be counted in the cumulative units toward graduation. Field Study: Read More [+]
Rules & Requirements
Prerequisites: Consent of instructor (see department adviser)
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of fieldwork per week
Summer: 6 weeks - 2.5-10 hours of fieldwork per week 8 weeks - 2-7.5 hours of fieldwork per week
Terms offered: Spring 2025, Fall 2024, Spring 2024
This course covers the fundamental circuit and device concepts needed to understand analog integrated circuits. After an overview of the basic properties of semiconductors, the p-n junction and MOS capacitors are described and the MOSFET is modeled as a large-signal device. Two port small-signal amplifiers and their realization using single stage and multistage CMOS building blocks are discussed. Sinusoidal steady-state signals are introduced and the techniques of phasor analysis are developed, including impedance and the magnitude and phase response of linear circuits. The frequency responses of single and multi-stage amplifiers are analyzed. Differential amplifiers are introduced. Microelectronic Devices and Circuits: Read More [+]
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 covers forward and inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics, and control. It presents elementary principles on proximity, tactile, and force sensing, vision sensors, camera calibration, stereo construction, and motion detection. The course concludes with current applications of robotics in active perception, medical robotics, and other areas. Introduction to Robotics: Read More [+]
Terms offered: Spring 2017, Spring 2016
This course is a sequel to Electrical Engineering C106A/Bioengineering C125, 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 [+]
Terms offered: Spring 2025, Spring 2024
This course is designed to provide an introduction to electric power conversion, distribution, and generation with renewable energy sources. The course will introduce fundamental concepts in the area of electric power, such as complex and reactive power, phasors, impedance, magnetic circuits and transformers, power factor, power
quality, three-phase power, ac-dc conversion, and a conceptual overview of renewable energy and the integration of sustainable energy sources on the electric grid.Real world applications, ranging from ground-fault circuit interrupters to residential power distribution and solar photovoltaic dc-ac inverters will be explored in
live lecture demonstration. Introduction to Electric Power and Renewable Energy: Read More [+]
Terms offered: Fall 2024, Fall 2023, Fall 2022
Power conversion circuits and techniques. Characterization and design of magnetic devices including transformers, reactors, and electromagnetic machinery. Characteristics of bipolar and MOS power semiconductor devices. Applications to motor control, switching power supplies, lighting, power systems, and other areas as appropriate. Power Electronics: Read More [+]
Rules & Requirements
Prerequisites:EL ENG 105 or background in circuit analysis (KVL, KCL, voltage/current relationships, etc.)
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 3 hours of laboratory per week
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 [+]
Rules & Requirements
Repeat rules: Course may be repeated for credit with instructor consent.
Hours & Format
Fall and/or spring: 15 weeks - 1.5 hours of lecture and 6 hours of laboratory per week
Grading/Final exam status: Letter grade. Alternate method of final assessment during regularly scheduled final exam group (e.g., presentation, final project, etc.).
Terms offered: Spring 2025, Spring 2024, Spring 2023
Review of static electric and magnetic fields and applications; Maxwell's equations; transmission lines; propagation and reflection of plane waves; introduction to guided waves, microwave networks, and radiation and antennas. Minilabs on statics, transmission lines, and waves. Explanation of cellphone antennas, WiFi communication, and other wireless technologies. Electromagnetic Fields and Waves: Read More [+]
Rules & Requirements
Prerequisites:EECS 16B, MATH 53, and MATH 54; PHYSICS 7B or equivalent that covers AC circuits and electromagnetics up to Maxwell's equations
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 2 hours of laboratory per week
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 [+]
Credit Restrictions: Students will receive no credit for Electrical Engineering 118 after taking Electrical Engineering 218A. A deficient grade in Electrical Engineering 119 may be removed by taking Electrical Engineering 118.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Terms offered: Spring 2025, Fall 2024, Spring 2024
Continuous and discrete-time transform analysis techniques with illustrative applications. Linear and time-invariant systems, transfer functions. Fourier series, Fourier transform, Laplace and Z-transforms. Sampling and reconstruction. Solution of differential and difference equations using transforms. Frequency response, Bode plots, stability analysis. Illustrated by analysis of communication systems and feedback control systems. Signals and Systems: Read More [+]
Terms offered: Spring 2016, Fall 2014, Fall 2013
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, and channel equalization. Applications to design of digital telephone modems, compact disks, and digital wireless communication systems. Concepts illustrated by a sequence of MATLAB exercises. Introduction to Digital Communication Systems: Read More [+]
Terms offered: Spring 2025, Spring 2024, Spring 2023
This course focuses on the fundamentals of the wired and wireless communication networks. The course covers both the architectural principles for making these networks scalable and robust, as well as the key techniques essential for analyzing and designing them. The topics include graph theory, Markov chains, queuing, optimization techniques, the physical and link layers, switching, transport, cellular networks and Wi-Fi. Introduction to Communication Networks: Read More [+]
Terms offered: Spring 2025, Spring 2024, Spring 2023
Discrete time signals and systems: Fourier and Z transforms, DFT, 2-dimensional versions. Digital signal processing topics: flow graphs, realizations, FFT, chirp-Z algorithms, Hilbert transform relations, quantization effects, linear prediction. Digital filter design methods: windowing, frequency sampling, S-to-Z methods, frequency-transformation methods, optimization methods, 2-dimensional filter design. Digital Signal Processing: Read More [+]
Terms offered: Spring 2017, Fall 2016, Spring 2016
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains. Probability and Random Processes: Read More [+]
Terms offered: Spring 2025, Spring 2024, Spring 2023
Analysis and synthesis of linear feedback control systems in transform and time domains. Control system design by root locus, frequency response, and state space methods. Applications to electro-mechanical and mechatronics systems. Feedback Control Systems: Read More [+]
Terms offered: Spring 2025, Fall 2024, Spring 2024
Overview of electronic properties of semiconductor. 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 [+]
Terms offered: Spring 2025, Spring 2024, Spring 2023
This course is designed to give an introduction to, 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 [+]
Rules & Requirements
Prerequisites:EECS 16A and EECS 16B; or Math 54 and Physics 7B; or equivalent
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
Terms offered: Fall 2024, Fall 2023, Fall 2022
Overview of conventional electric power conversion and delivery, emphasizing a systemic understanding of the electric grid with primary focus at the transmission level, aimed toward recognizing needs and opportunities for technological innovation. Topics include aspects of a.c. system design, electric generators, components of transmission and distribution systems, power flow analysis, system planning and operation, performance measures, and limitations of legacy technologies. Introduction to Electric Power Systems: Read More [+]
Terms offered: Spring 2023, Spring 2022, Spring 2021
Overview of recent and potential future evolution of electric power systems with focus on new and emerging technologies for power conversion and delivery, primarily at the distribution level. Topics include power electronics applications, solar and wind generation, distribution system design and operation, electric energy storage, information management and communications, demand response, and microgrids. Introduction to Electric Power Systems: Read More [+]
Rules & Requirements
Prerequisites:EL ENG 137A or consent of instructor
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week
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. Linear Integrated Circuits: Read More [+]
Terms offered: Spring 2025, Spring 2024, Fall 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 [+]
Terms offered: Spring 2025, Fall 2024, Spring 2024
Integrated circuit device fabrication and surface micromachining technology. Thermal oxidation, ion implantation, impurity diffusion, film deposition, expitaxy, lithography, etching, contacts and interconnections, and process integration issues. Device design and mask layout, relation between physical structure and electrical/mechanical performance. MOS transistors and poly-Si surface microstructures will be fabricated in the laboratory and evaluated. Microfabrication Technology: Read More [+]
Terms offered: Spring 2025, Fall 2015, Fall 2014
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 tools. Fundamental Algorithms for Systems Modeling, Analysis, and Optimization: Read More [+]
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 [+]
Terms offered: Fall 2014, Fall 2013, Fall 2012
Laboratory exercises exploring a variety of electronic transducers for measuring physical quantities such as temperature, force, displacement, sound, light, ionic potential; the use of circuits for low-level differential amplification and analog signal processing; and the use of microcomputers for digital sampling and display. Lectures cover principles explored in the laboratory exercises; construction, response and signal to noise of electronic transducers and actuators; and design of circuits for sensing and controlling physical quantities. Introductory Electronic Transducers Laboratory: Read More [+]
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 3 hours of laboratory per week
Terms offered: Spring 2013, Spring 2012, Spring 2011
Laboratory exercises constructing basic interfacing circuits and writing 20-100 line C programs for data acquisition, storage, analysis, display, and control. Use of the IBM PC with microprogrammable digital counter/timer, parallel I/O port. Circuit components include anti-aliasing filters, the S/H amplifier, A/D and D/A converters. Exercises include effects of aliasing in periodic sampling, fast Fourier transforms of basic waveforms, the use of the Hanning filter for leakage reduction, Fourier analysis of the human voice, digital filters, and control using Fourier deconvolution. Lectures cover principles explored in the lab exercises and design of microcomputer-based systems for data acquisitions, analysis and control. Introductory Microcomputer Interfacing Laboratory: Read More [+]
Rules & Requirements
Prerequisites: EE 16A & 16B
Hours & Format
Fall and/or spring: 15 weeks - 2 hours of lecture and 3 hours of laboratory per week
Terms offered: Spring 2015, Spring 2014, Spring 2013, Spring 2012
Introduction to laboratory and field study of the biomechanics of animals and plants using fundamental biomechanical techniques and equipment. Course has a series of rotations involving students in experiments demonstrating how solid and fluid mechanics can be used to discover the way in which diverse organisms move and interact with their physical environment. The laboratories emphasize sampling methodology, experimental design, and statistical interpretation of results. Latter third of course devoted to independent research projects. Written reports and class presentation of project results are required. Laboratory in the Mechanics of Organisms: Read More [+]
Terms offered: Spring 2015
This is a lab course that covers the design of modern Application-Specific Integrated Circuits (ASICs). The labs lay the foundation of modern digital design by first setting-up the scripting and hardware description language base for specification of digital systems and interactions with tool flows. Software testing of digital designs is covered leading into a set of labs that cover the design flow. Digital synthesis, floorplanning, placement and routing are covered, as well as tools to evaluate design timing and power. Chip-level assembly is covered, instantiation of custom IP blocks: I/O pads, memories, PLLs, etc. The labs culminate with a project design – implementation of a 3-stage RISC-V processor with register file and caches. Application Specific Integrated Circuits Laboratory: Read More [+]
Objectives & Outcomes
Course Objectives: This course is a one-time offering to supplement the CS150 course offered in the Fall 2014, with a lab and project section that cover the Application-Specific Integrated Circuit Design. The CS150 lectures in the Fall 2014 already covered the necessary lecture material, so students who took the CS150 lab in the Fall of 2014 will have a chance to expand their skills into the area of Application-Specific Integrated Circuit design.
Hence the pre-requisite for this course is that a student has taken the CS150 course in the Fall 2014.
Credit Restrictions: Students will receive no credit for Electrical Engineering 146L after taking Fall 2014 version of Electrical Engineering 141/241A.
Hours & Format
Fall and/or spring: 15 weeks - 3 hours of laboratory and 1 hour of discussion per week
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 [+]
Terms offered: Spring 2021, Spring 2020, Spring 2019
Design project course, focusing on application of theoretical principles in electrical engineering to control of a small-scale system, such as a mobile robot. Small teams of students will design and construct a mechatronic system incorporating sensors, actuators, and intelligence. Mechatronic Design Laboratory: Read More [+]
Terms offered: Spring 2025, Fall 2024, Spring 2024
Topics will vary semester to semester. See the Electrical Engineering announcements. Special Topics: Read More [+]
Rules & Requirements
Prerequisites: Consent of instructor
Repeat rules: Course may be repeated for credit when topic changes.
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of lecture per week
Terms offered: Spring 2016, Fall 2015, Spring 2015
Thesis work under the supervision of a faculty member. A minimum of four units must be taken; the units may be distributed between one and two semesters in any way. To obtain credit a satisfactory thesis must be submitted at the end of the two semesters to the Electrical and Engineering and Computer Science Department archive. Students who complete four units and a thesis in one semester receive a letter grade at the end of H196A. Students who do not, receive an IP in H196A and must enroll in H196B. Senior Honors Thesis Research: Read More [+]
Rules & Requirements
Prerequisites: Open only to students in the Electrical Engineering and Computer Science honors program
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of independent study per week
Grading/Final exam status: Letter grade. This is part one of a year long series course. A provisional grade of IP (in progress) will be applied and later replaced with the final grade after completing part two of the series. Final exam required.
Terms offered: Spring 2016, Spring 2015, Spring 2014
Thesis work under the supervision of a faculty member. A minimum of four units must be taken; the units may be distributed between one and two semesters in any way. To obtain credit a satisfactory thesis must be submitted at the end of the two semesters to the Electrical and Engineering and Computer Science Department archive. Students who complete four units and a thesis in one semester receive a letter grade at the end of H196A. Students who do not, receive an IP in H196A and must enroll in H196B. Senior Honors Thesis Research: Read More [+]
Rules & Requirements
Prerequisites: Open only to students in the Electrical Engineering and Computer Science honors program
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of independent study per week
Grading/Final exam status: Letter grade. This is part two of a year long series course. Upon completion, the final grade will be applied to both parts of the series. Final exam required.
Terms offered: Fall 2023, Fall 2022, Fall 2021
Students take part in organized individual field sponsored programs with off-campus companies or tutoring/mentoring relevant to specific aspects and applications of computer science on or off campus. Note Summer CPT or OPT students: written report required. Course does not count toward major requirements, but will be counted in the cumulative units toward graduation. Field Study: Read More [+]
Rules & Requirements
Prerequisites: Consent of instructor (see department adviser)
Repeat rules: Course may be repeated for credit without restriction.
Hours & Format
Fall and/or spring: 15 weeks - 1-4 hours of fieldwork per week
Summer: 6 weeks - 2.5-10 hours of fieldwork per week 8 weeks - 2-7.5 hours of fieldwork per week
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