Updated July 29, 2021
This page serves the purpose to help graduate students understand each graduate course offered during the 2021-2022 academic year. Please use this page as a guideline to help decide what courses to take.
Fall 2021 Graduate Course Information
CSE 200 - Computability and Complexity with Prof. Shachar Lovett
Description: This class is an introduction to computational complexity theory. This area aims to understand the various resources needed to solve computational problems. Such resources include the obvious ones, such as time and memory, but also possibly less obvious ones, such as randomization, interaction, and non-uniformity. In addition, we will attempt to classify problems as "easy" or "hard", study relations between easy and hard problems, as understand why the "P vs NP" problem is possibly the most important open problem in computer science, mathematics, and science at large.
Required Knowledge: Undergraduate computability theory: DFAs, Turing machines, decidability, and undecidability. Undergraduate basic algorithms class: graph algorithms (eg BFS/DFS), dynamic programming, etc.
Enforced Prerequisite: Yes. CSE 101 or 105 (or equivalent).
Recommended Preparation for Those Without Required Knowledge: Sipser - theory of computation (covers core computability theory)
Link to Past Course: http://cseweb.ucsd.edu/classes/wi18/cse200-a/
CSE 202 - Algorithm Design and Analysis with Prof. Mohan Paturi
Description: Please see the website below for all relevant course information.
Required Knowledge: Please see the website below for all relevant course information.
Enforced Prerequisite: Yes. CSE 101 or equivalent.
Recommended Preparation for Those Without Required Knowledge: Please see the website below for all relevant course information.
Link to Past Course: https://sites.google.com/eng.ucsd.edu/algorithms/cse202
CSE 206A - Lattice Algorithms & Applications with Prof. Daniele Micciancio
Description: Point lattices are powerful mathematical objects that can be used to efficiently solve many important problems in computer science, most notably in the areas of cryptography and combinatorial optimization. This course gives a general introduction to the theory of point lattices, their algorithms, computational complexity, mathematical techniques, and applications to cryptography.
Required Knowledge: The main prerequisites for the course are general mathematical maturity, knowledge of basic mathematics (good linear algebra and probability theory, basic abstract algebra, and a little bit of calculus) and introductory level algorithms and complexity theory (mathematical models of computation, analysis of algorithms, polynomial time solvability, NP-hardness, etc.) Some prior knowledge of cryptography is useful, but not strictly required. No prior knowledge of advanced complexity theory, Fourier analysis, or algebraic number theory is assumed, but you should be prepared to learn a bit about all this through the course.
Enforced Prerequisite: None, but students are expected to have already taken CSE 200 and CSE 201 or equivalent and have the implied prerequisites (i.e. good discrete math, linear algebra, probability).
Recommended Preparation for Those Without Required Knowledge: CSE 200
Link to Past Course: http://cseweb.ucsd.edu/classes/fa19/cse206A-a/
CSE 210 - Principles of Software Engineering with Prof. Bill Griswold
Description: Building a great, successful software system is an intellectually challenging activity that is more likely to fail than succeed. Yet, software is becoming increasingly sophisticated and now permeates every aspect of our lives. In short, software is becoming harder to build, but it's increasingly important to build it right.
The methods for creating software have changed dramatically to keep pace. This course is an introduction to the state of the art and hot trends in software development. This course will not only introduce the salient concepts of software engineering, but it will also introduce valuable practices and develop our skills in applying them through a small team project (several projects have gone into production over the years). We will study insightful case studies such as the Mythical Man Month and Microsoft Secrets, as well as classic and leading-edge articles from the research literature, including XP and Design Patterns.
This course is run as a seminar (albeit at a large scale), requiring daily preparation (with hand-ins) and constant, deep participation. There is also a project with weekly milestones and a project presentation at the end.
Required Knowledge: An undergraduate degree in computer science (or equivalent) is a prerequisite, but an undergraduate software engineering class is not.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: These courses would provide adequate preparation: CSE 100 and one of CSE 131 or CSE 110.
Link to Past Course: https://cseweb.ucsd.edu/~wgg/CSE210/
CSE 221 - Operating Systems with Prof. YY Zhou
Description: The purpose of this course is to learn about a wide variety of operating system design and implementation principles and techniques, examining their usage in the context of both important historical systems as well as modern systems. In addition, students learn how to read research papers in a critical manner, how to articulate an understanding of and insights into material described in research papers, and how to synthesize research themes and topics across multiple systems. The topics covered include operating system structures, synchronization, virtual memory, distribution, interaction of architecture and systems, scheduling, communication, file systems, multicore scalability, and virtual machine monitors.
Required Knowledge: Undergraduate operating systems.
Enforced Prerequisite: Yes. CSE 120 or equivalent.
Recommended Preparation for Those Without Required Knowledge: CSE 120 or equivalent
Link to Past Course: http://cseweb.ucsd.edu/classes/fa18/cse221-a/
CSE 227 - Computer Security with Prof. Stefan Savage
Description: This course focuses on computer and network security, exploring a range of topics design to illustrate some of the modern research challenges in the area and the standards for advancement. It is not designed to be a tutorial course, but rather to give students the context to understand current security research and evaluate their interest in the field. We will explore a range of topics on both the defensive and offensive side of the field, as illustrated by papers and via lecture. This class will have two primary means of evaluation: classroom participation and a quarter-long research project of your own choosing with results presented both in writing and orally.
Required Knowledge: N/A
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: http://cseweb.ucsd.edu/classes/fa19/cse227-a/
CSE 230 - Principles/Program Languages with Prof. Ranjit Jhala
Description: The goal of this class is to expose students to advanced programming language ideas, including high-level programming abstractions, expressive type systems and program analyses. We will develop these ideas in roughly two parts.
First, we will see how the lambda calculus can be used to distill the essence of computation into a few powerful constructs, and we will use it as a launching pad to study expressive type systems, logics, and analyses that can make precise predictions about run-time behavior at compile time.
Second, we will study how this calculus yields Haskell, a functional programming language that has been the incubator for many recent PL advances. We will use Haskell to learn about a variety of high-level programming abstractions and techniques.
Required Knowledge: Basic discrete math (sets, boolean algebra, induction etc.).
Enforced Prerequisite: None, but CSE 130 is recommended.
Recommended Preparation for Those Without Required Knowledge: Be familiar with the sort of topics covered in CSE 20, CSE 105 and CSE 130.
Link to Past Course: http://ucsd-pl.github.io/cse230/
CSE 232 - Principles/Database Systems with Prof. Yannis Papakonstantinou
Description: CSE232 covers the internals of database management systems. It covers
(1) storage and indexing aspects,
(2) query execution and optimization,
(3) transaction processing (both concurrency control and failure recovery) and
(4) aspects and features of modern database systems
The students will obtain in-depth knowledge of the workings of classic SQL-based database systems and will also learn selected aspects of modern database systems.
The class equips students with the background knowledge needed to pursue further classes in modern database systems and database theory.
Required Knowledge: CSE232 focuses on the internals of database systems. The class is NOT an introduction to SQL or database schema design. The students are expected to know SQL and database schema design in advance.
Enforced Prerequisite: Yes. Knowledge of database schema design and SQL querying, as provided in CSE132A or similar classes at other universities or extensive industry experience. The prerequisite is enforced by an exam in the second week of classes.
Recommended Preparation for Those Without Required Knowledge: Self-study of database schema design and SQL querying. We recommend using Garcia, Ullman, Widom "Database Systems: The Complete Book", since the second part of the book is the class textbook.
Link to Past Course: N/A
CSE 234 - Data Systems for ML with Prof. Arun Kumar
Description: This is a research-based course on data systems for machine learning, at the intersection of the fields of ML/AI, data management, and systems. Such systems power modern data science applications on large and complex datasets, including enterprise analytics, recommendation systems, and social media analytics. Students will learn about the landscape and evolution of such systems and the latest research. This is a lecture-driven course involving a mandatory paper reviewing component for all students. There will be two alternate pathways for additional learning evaluation components: one based on written quizzes and exams and the other based on a research or survey project ending with a report and presentation. Students must pick a pathway early on in the class. Both pathways can be pursued and if so, the better of the two grades will be used. This course is aimed primarily at MS students interested in the state of the art of systems for scalable data science and ML engineering, as well as PhD students interested in research in this space.
Required Knowledge: A course on ML algorithms is absolutely necessary. A course on either database systems or operating systems is also necessary. These courses could have been taken at UCSD or elsewhere.
Enforced Prerequisite: Yes. A mainstream ML algorithms course is needed (e.g., CSE 150 or 250B). Either a mainstream DB system internals course (CSE 132C or 232A) or a mainstream OS course (e.g., CSE 120 or 220) is also needed. Alternatively, DSC 102 will suffice on both counts. Another alternative is substantial prior industrial/real-world project experience with scalable ML systems, subject to the consent of the instructor.
Recommended Preparation for Those Without Required Knowledge: Read these 4 recommended textbooks for background/foundations on the respective component areas: "Machine Learning" (McGraw Hill), "Deep Learning" (MIT Press), "Database Management Systems" (McGraw Hill), and "Operating Systems: Three Easy Pieces".
Link to Past Course: http://cseweb.ucsd.edu/classes/fa20/cse291-d/
CSE 237C - Validation & Testing/EmbeddedSys with Prof. Ryan Kastner
Description: Please see the website below for all relevant course information.
Required Knowledge: Please see the website below for all relevant course information.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Please see the website below for all relevant course information.
Link to Past Course: http://kastner.ucsd.edu/ryan/cse237c/
CSE 240A - Princ/Computer Architecture with Prof. Jishen Zhao
Description: This course provides a thorough and fundamental treatment of the art of computer architecture. Topics include concepts of von Neumann architectures, methods of evaluating CPU performance, instruction-set design and examples, pipelining, branch prediction, cache and memory hierarchy, main memory organization and memory controller, virtual memory, multicore processors, cache coherence and memory consistency, and GPU architecture.
Required Knowledge: Basics of Instruction Set Architecture, cache design, and the design of single-cycle, multi-cycle, and pipeline processors.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: If you do not have the appropriate background, you should either 1) not take this class or 2) spend significant time reviewing the textbook and lecture notes from CSE141.
Link to Past Course: https://cseweb.ucsd.edu/classes/fa17/cse240A-a/
CSE 243A - Intr Synthesis Method VLSI CAD with Prof. Alex Orailoglu
Description:
Required Knowledge:
Enforced Prerequisite:
Recommended Preparation for Those Without Required Knowledge:
Link to Past Course:
CSE 250A - Probabilistic Reasoning & Learning with Prof. Lawrence Saul
Description: Probabilistic methods for reasoning and decision-making under uncertainty. Topics include: inference and learning in directed probabilistic graphical models; prediction and planning in Markov decision processes; applications to computer vision, robotics, speech recognition, natural language processing, and information retrieval.
Required Knowledge: The course is aimed broadly at advanced undergraduates and beginning graduate students in mathematics, science, and engineering. Prerequisites are elementary probability, multivariable calculus, linear algebra, and basic programming ability in some high-level language such as Python, Matlab, Java, or C. Programming assignments are completed in the language of the student's choice.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Review of multivariable calculus and linear algebra.
Link to Past Course: http://cseweb.ucsd.edu/classes/fa20/cse250A-a/
CSE 252A - Computer Vision I with Prof. Ben Ochoa
Description: This course provides a comprehensive introduction to computer vision providing broad coverage including low-level vision (image formation, photometry, color, image features), inferring 3D properties from images (shape-from-shading, stereo vision, motion interpretation) and object recognition.
Required Knowledge: Linear algebra, calculus, data structures, and probability and statistics. Programming assignments will be in Python.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Undergraduate courses and textbooks.
Link to Past Course: https://cseweb.ucsd.edu/classes/fa19/cse252A-a/
CSE 257 - Search and Optimization with Prof. Sean Gao
Description: The course will cover core search and optimization algorithms for decision-making. Topics include heuristic search, deep reinforcement learning, Monte Carlo tree search, numerical optimization, and combinatorial search.
Required Knowledge: Have previously taken at least one course in AI or machine learning.
Enforced Prerequisite: None for graduate students. Undergraduate students must have taken CSE 150B.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: https://scungao.github.io/ucsd-s21/index.html
CSE 258 - Recommender Systems & Web Mining with Prof. Julian McAuley
Description: CSE 258 is a graduate course devoted to current methods for recommender systems, data mining, and predictive analytics.
Required Knowledge: Students should be comfortable with programming (all example code will be in Python), and with basic optimization and linear algebra.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Follow the chapter references for the first three weeks of the course for background material on each of the introductory topics covered.
Link to Past Course: http://cseweb.ucsd.edu/classes/fa20/cse258-a/
CSE 260 - Parallel Computation with Prof. Bryan Chin (CLASS CANCELLED FOR QUARTER)
Description: Exploration of programming for parallel computing systems such as shared memory systems, message passing systems and highly parallel accelerators (like GPUs). The course will cover the principles of parallel programming by exploring algorithm design and how those design choices are affected by the machine architecture.
Required Knowledge: This is a graduate-level course. Students should have strong programming ability in C (and familiarity with C++).
Enforced Prerequisite: None, but familiarity with computer architecture is required.
Recommended Preparation for Those Without Required Knowledge: Class in programming which requires the use of C or strong programming skills in similar languages. Review computer architecture - especially memory system design (e.g. cache hierarchy). Contact the instructor for more help or advice.
Link to Past Course: https://sites.google.com/eng.ucsd.edu/cse260fall2020/home
CSE 274 - Selected Topics in Graphics with Prof. Ravi Ramamoorthi
Description: This is a graduate course in selected topics in computer graphics. This quarter, we will cover Sampling and Reconstruction of Visual Appearance. The course will focus on two main areas of considerable current interest: Monte Carlo Denoising and View Synthesis.
Required Knowledge: CSE 167 (Computer Graphics) or equivalent at another university
Enforced Prerequisite: Yes. CSE 167 or equivalent.
Recommended Preparation for Those Without Required Knowledge: Take my CSE 167x course on edX (be prepared to provide verification to instructor).
Link to Past Course: http://cseweb.ucsd.edu/~viscomp/classes/cse274/wi18/274.html
CSE 276A - Introduction to Robotics with Prof. Henrik Christensen
Description: The desired learning outcomes for the students are:
- Understanding basic and advanced concepts in robotics
- Understanding the most common techniques used in the field
- Applying them on one or more robotic platforms and gain experience
- Interacting with your peers about the material, polls, quizzes, and assignments
- Evaluating your own progress in the course on a regular basis
Textbook: Robotics, Vision & Control, Peter Corke, Springer Verlag. (see its webpage for links)
Required Knowledge: This is an introductory graduate class, so a minimum of formal requirements is assumed beyond an ability to program.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: http://www.hichristensen.com/CSE276A-20/
CSE 276C - Mathematics for Robotics with Prof. Henrik Christensen
Description: This is a graduate course that introduces the mathematical tools used in many diverse areas of robotics. The class is inspired by the equivalent class at Carnegie Mellon University by Michael Erdmann - 811 Mathemetical Foundations for Robotics
The goal of the course is to help you accomplish the following:
- Understand the geometry of linear systems of equations, including null spaces, row spaces and column spaces. Understand eigenvalues, diagonalization, singular value decomposition, least-squares solutions. Apply these skills to infer rigid-body transformations from data.
Learn how to model data using interpolation, linear regression, and higher-order approximation methods. Consider these skills to recognize objects from point-cloud data.
- Understand different methods for finding roots of equations, in one dimension and higher, including bisection, Newton's method, Müller's method. Develop skills to analyze convergence rates.
- Generalize finite-dimensional linear algebra concepts such as orthogonal bases to perform function approximation.
- Develop techniques for solving differential equations numerically.
- Understand optimization techniques such as gradient descent, conjugate gradient, Newton's method. Learn the basics of convex optimization. Consider obstacle-avoidance viewed as cost-minimization.
- Develop methods from the Calculus of Variations for higher-order optimization. Apply these techniques to develop the Lagrangian dynamics of a robot manipulator.
- Learn basic computational geometry techniques, such as Convex Hull and Voronoi diagrams. Implement a geometric robot motion planner.
Required Knowledge: A good foundation of probability and linear algebra. This class will have more math in it than most Computer Science classes. A good working knowledge of Matlab or Python with Numpy. We will be doing things in python as part of the class.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: http://www.hichristensen.com/CSE276C-20/
CSE 291 (1) - Unsupervised Learning with Prof. Sanjoy Dasgupta
Description: This course presents a broad view of unsupervised learning. Topics covered will include: descriptive statistics; clustering; projection, singular value decomposition, and spectral embedding; common probability distributions; density estimation; graphical models and latent variable modeling; sparse coding and dictionary learning; autoencoders, shallow and deep; and self-supervised learning. The focus throughout will be on understanding the modeling assumptions behind different methods, their statistical and algorithmic characteristics, and common issues that arise in practice.
Required Knowledge: There are no enforced prerequisites. The ideal preparation is a combination of CSE 250A and either CSE 251A or CSE 258; but at the very least, an undergraduate-level background in probability, linear algebra, and algorithms is essential.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: CSE 250A, CSE 251A, CSE 258.
Link to Past Course: N/A
CSE 291 (2) - Introduction to Computer Science Education Research with Prof. Leo Porter
Description: (Please note: this course is currently suppressed and enrollment is closed.) Computer science as a major has high societal demand. In addition, computer programming is a skill increasingly important for all students, not just computer science majors. However, computer science remains a challenging field for students to learn. This course examines what we know about key questions in computer science education: Why is learning to program so challenging? What pedagogical choices are known to help students? How do those interested in Computer Science Education Research (CSER) study and answer pressing research questions?
This course surveys the key findings and research directions of CSER and applications of those findings for secondary and post-secondary teaching contexts. We study the development of the field, current modes of inquiry, the role of technology in computing, student representation, research-based pedagogical approaches, and important open research questions. CSER is a relatively new field and there is much to be done; an important part of the course engages students in the design phases of a CSER study and asks students to complete a significant project (e.g., a review of an area of CSER research, prototyping of a software system to aid student learning).
Required Knowledge: None.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: None.
Link to Past Course: N/A
CSE 291 (3) - Internet Infrastructure with Prof. Aaron Schulman
Description: The Internet's success can largely be attributed to the simple abstraction it provides: any host can send packets to any other host, on any type of link, anywhere in the world. Hidden underneath this simple abstraction, the Internet is made of a vast network of heterogeneous infrastructure. In this course, we will read and discuss the latest research on the infrastructure behind the Internet. Topics will include wireless Internet access links that provide high performance in increasingly challenging environments, including 5G mobile, rural ISPs, and Low Earth Orbit satellite networks. Also, we will discuss large-scale wireline Internet infrastructure, including metropolitan and long-distance fiber, Fiber-To-The-Home, and Fiber-To-The-Node (e.g., DOCSIS).
Required Knowledge: Minimum Undergraduate Networking, Graduate Networking Preferred
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: None.
Link to Past Course: N/A
CSE 291 (4) - Towards Human-Centered AI with Prof. Nadir Weibel
Description: In this class we will discuss topics at the intersection of AI and Design, specifically focusing on important points at this intersection that should be part of computer science and data science education.
Required Knowledge: None.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: N/A
CSE 291 (5) - Deep Reinforcement Learning with Prof. Rose Yu
Description: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research is at the forefront of machine learning. Deep RL is able to solve a wide range of complex decision-making tasks, opening up new opportunities in domains such as healthcare, robotics, smart grids, finance, and many more. This class will cover recent advances in deep RL, including imitation learning, inverse RL, Policy Gradients, Deep Q-learning, Actor-Critic algorithms, model-based RL, and meta-learning. The course will be a combination of lectures, student presentations, and projects.
Required Knowledge: Familiarity with machine learning, deep learning, reinforcement learning, and numerical optimization.
Enforced Prerequisite: Yes. 1. CSE 150B (or equivalent) & CSE 151A (or equivalent), or CSE 250A; and 2. CSE 151B (or equivalent)
Recommended Preparation for Those Without Required Knowledge: François-Lavet, V., Henderson, P., Islam, R., Bellemare, M.G. and Pineau, J., 2018. An Introduction to Deep Reinforcement Learning. Foundations and Trends® in Machine Learning, 11(3-4), pp.219-354.
Link to Past Course: First-time offering