Updated February 12, 2020
This page serves the purpose to help graduate students understand each graduate course offered during the 2019-2020 academic year. Please use this page as a guideline to help decide what courses to take.
Spring 2020 Graduate Course Information
CSE 201A - Advanced Complexity with Prof. Russell Impagliazzo
Description: This class picks up where CSE 200 leaves off, presenting advances in computational complexity since the 1980s. In particular, we will consider the impact of the resolution of open problems in complexity to cryptography, machine learning, approximation algorithms, heuristics, and derandomization.
Required Knowledge: We assume students have had a previous graduate course in basic complexity, such as CSE 200, and have some general mathematical maturity, including the ability to recognize and write a valid proof. If your background in these areas is weak, you may need to do some extra work to catch up. Please talk to me about this.
Enforced Prerequisite: Yes. Students who have not taken CSE 200 or the equivalent need permission from the instructor.
Recommended Preparation for Those Without Required Knowledge: CSE 200 is being offered in Winter 2020. Students could audit this class.
Link to Past Course: https://cseweb.ucsd.edu/classes/sp11/cse201A-a/
CSE 218 - Adv Topic/Software Engineering with Prof. Bill Griswold
Description: Programmers and software designers/architects are often concerned about the modularity of their systems, because effective modularity reaps a host of benefits for those working on the system, including ease of construction, ease of change, and ease of testing, to name just a few. Other possible benefits are reuse (e.g., in software product lines) and online adaptability.
This course will be an open exploration of modularity - methods, tools, and benefits. In the process, we will confront many challenges, conundrums, and open questions regarding modularity.
In this class, we will explore defensive design and the tools that can help a designer redesign a software system after it has already been implemented. Defensive design techniques that we will explore include information hiding, layering, and object-oriented design. In the area of tools, we will be looking at a variety of pattern matching, transformation, and visualization tools.
Each week there will be assigned readings for in-class discussion, followed by a lab session. Each week, you must engage the ideas in the Thursday discussion by doing a "micro-project" on a common code base used by the whole class: write a little code, sketch some diagrams or models, restructure some existing code or the like.
Required Knowledge: Students must satisfy one of:
1. Be a CSE graduate student. It's also recommended to have either:
(a) programming experience through CSE 100 Advanced Data Structures (or equivalent), or
(b) substantial software development experience, or
(c) CSE 210.
or
2. Have graduate status and have either:
(a) programming experience up through CSE 100 Advanced Data Structures (or equivalent), or
(b) substantial software development experience, or
(c) CSE 210.
Equivalents and experience are approved directly by the instructor.
Enforced Prerequisite: See above
Recommended Preparation for Those Without Required Knowledge: See above
Link to Past Course: https://cseweb.ucsd.edu/~wgg/CSE218/index.html
CSE 223B - Distributed Computing and Systems with Prof. Alex Snoeren
Description: CSE 223B is a 4-unit graduate subject with lectures, paper discussions, labs, a midterm, a final, and a term project. It will present abstractions and implementation techniques that facilitate the design of distributed systems--including both operating systems and sophisticated Internet servers--that can deal with the demands of real-world workloads. Topics will include efficient operating system primitives, high-performance network servers, load shedding, storage systems, security, and fault tolerance.
Required Knowledge: CSE 221 (or consent of instructor) and substantial programming experience for lab assignments and term project. Note that CSE 223A is not a prerequsite. However, those interested in the theoretical underpinnings of distributed computation are encouraged to take CSE223A as well. CSE223A and CSE223B may be taken in any order.
Enforced Prerequisite: Yes. CSE 221 or instructor consent
Recommended Preparation for Those Without Required Knowledge: CSE221 / CSE222A
Link to Past Course: http://cseweb.ucsd.edu/classes/sp18/cse223B-a/
CSE 227 - Computer Security with Prof. Deian Stefan
Description: This course focuses on computer security, exploring a range of topics – from systems security to web security, IoT security, and privacy – 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. The course will examine both the defensive and offensive side of the field. At the conclusion of the course, the student will have the foundation to conduct research in computer security and to apply the latest security research to a particular area of practice.
Required Knowledge: Undergrad security + some OS class or some PL class
Enforced Prerequisite: Yes. Two classes from the following choices: CSE 127, CSE 120/221, and CSE 130/230
Recommended Preparation for Those Without Required Knowledge: Take CSE 120, CSE 127, CSE 130, CSE 221, and/or CSE 230
Link to Past Course: N/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 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 233 - Database Theory with Prof. Victor Vianu
Description: This course presents an overview of the theory of databases. Topics include the theory of query languages, dependency theory, deductive databases, incomplete information, complex objects, semistructured data, and other advanced topics and research issues as time allows. Connections are made to relevant areas in logic and complexity theory. Evaluation is based on homework sets and a take-home final.
Required Knowledge: Students should have had at least one basic database systems course including SQL. The course is theoretical, and mathematical maturity is highly desirable. Basic knowledge of computability and complexity theory is assumed.
Enforced Prerequisite: Yes. CSE 132A (or equivalent) and CSE 200 (or equivalent). Instructor will consider waivers on an individual basis.
Recommended Preparation for Those Without Required Knowledge: An undergrad database text, emphasizing SQL. Sipser's text on Introduction to the Theory of Computation.
Link to Past Course: N/A
CSE 237D - Design Automation & Prototyping with Prof. Ryan Kastner
Description: This is an embedded systems project course. You will work on teams on either your own project (with instructor approval) or ongoing projects. Examples from previous years include remote sensing, robotics, 3D scanning, wireless communication, and embedded vision. You can browse examples from previous years for more detailed information. The class time discussions focus on skills for project development and management. We discuss how to give presentations, write technical reports, present elevator pitches, effectively manage teammates, entrepreneurship, etc.. The grading is primarily based on your project with various tasks and milestones spread across the quarter that are directly related to developing your project. Each project will have multiple presentations over the quarter. The class ends with a final report and final video presentations.
Required Knowledge: A general understanding of some aspects of embedded systems is helpful but not required. The desire to work hard to design, develop, and deploy an embedded system over a short amount of time is a necessity.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Contact Professor Kastner as early as possible to get a better understanding for what is expected and what types of projects will be offered for the next iteration of the class (they vary substantially year to year). This is particularly important if you want to propose your own project.
Link to Past Course: http://kastner.ucsd.edu/ryan/cse-237d-embedded-system-design/
CSE 240C - Advanced Microarchitecture with Prof. Hadi Esmaeilzadeh
Description: This incarnation of the CSE 240C will focus on Accelerated Architecture Design for Machine Learning and Artificial Intelligence.
A wide range of commercial and enterprise applications such as health monitoring, social networking, e-commerce, and financial analysis, rely on Machine Learning (ML) to accomplish their objectives. In fact, the advances in machine learning are changing the landscape of computing towards a more personalized and targeted experience for users. For instance, services that provide personalized health-care and targeted advertisements are either prevalent or on the horizon. Nevertheless, machine learning algorithms are computationally intensive workloads. Specifically, learning a model from data requires substantial amount of computation that is repeated over the training data for a relatively large number of iterations. While the demand for these computationally intensive techniques is increasing, the benefits from general-purpose solutions is diminishing. With the effective end of Dennard scaling, traditional CMOS scaling no longer provides performance and efficiency gains commensurate with increases in transistor density. The current paradigm of general-purpose processor design falls significantly short of the traditional cadence of performance improvements. These challenges have coincided with the explosion of data where the rate of data generation has reached an overwhelming level that is beyond the capabilities of current computing systems to match. As a result, both the industry and the research community are focusing on programmable accelerators, which can provide large gains in efficiency and performance by restricting the workloads they support. In this course, we will first thoroughly examine these trends to understand the underlying research challenges and opportunities. Second, we will devise novel technologies to build the foundation of next generation computing systems for artificial sentience and consciousness.
The course will be a combination of lectures, student presentation, and separate brainstorming sessions in which we will collectively explore and develop new ideas on each topic. The students will do a project based on one of these ideas. There is no required textbook. All relevant materials will be made available online.
Required Knowledge: The students are expected to be comfortable with a) computer architecture, and b) programming in a language such as Python or C/C++.
Enforced Prerequisite: None
Recommended Preparation for Those Without Required Knowledge: Through the study of computer architecture and mastering programming.
Link to Past Course: https://hadiclass.github.io/cse240c-wi18/index.html
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://hadiclass.github.io/cse240a-fa18/index.html
CSE 252C - Advanced Computer Vision with Prof. Manmohan Chandraker
Description: This course will cover advanced concepts in computer vision and focus on recent developments in the field. Example topics include 3D reconstruction, object detection, semantic segmentation, multi-target tracking, action recognition, reflectance estimation, image captioning and domain adaptation. The class will be composed of lectures by the instructor, but with a participation element too where students will engage through lightning presentations. Grading will be based on a participation, mid-term, final exam and course project.
Required Knowledge: Previous experience with computer vision and deep learning is required. Programming experience in Python is required.
Enforced Prerequisite: Yes. Students who have taken 2 out of the following courses are allowed to enroll directly: 250B, 252A, 252B, 253, 254.
For students who have taken only 1 of those courses, please put on waitlist and student should send the instructor an email, with subject “SP20 CSE 252C: Request to enroll.” The email should contain description of their prior coursework and project experience relevant to computer vision.
Recommended Preparation for Those Without Required Knowledge: CSE 252A, CSE 252B, graduate machine learning classes offered in the CSE and COGS departments.
Link to Past Course: http://cseweb.ucsd.edu/~mkchandraker/classes/CSE252C/Spring2019/
CSE 276D - Healthcare Robotics with Prof. Laurel Riek
Description: Robotics has the potential to improve well-being for millions of people, support caregivers, and aid the clinical workforce. This course brings together engineers, clinicians, and end-users to explore this exciting new field. It is project-based, interactive, and hands-on, and involves working with stakeholders to develop prototypes that solve real-world problems. Students will explore the latest research in healthcare robotics, human-robot teaming, and health design.
Required Knowledge: This course will involve design thinking, physical prototyping, and software development. Students with backgrounds in engineering should be comfortable with building and experimenting within their area of expertise. (e.g., CSE students should be experienced in software development, MAE students in rapid prototyping, etc.). Students with backgrounds in design should be comfortable employing mixed-methods approaches to designing with stakeholders. All students should be open to learning how to work with stakeholders from the community, including clinicians and people with disabilities.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Any of the following: CSE 276A, CSE 276B, CSE 216, CSE 219, MAE 207, MED 265, CLRE 252
Link to Past Course: N/A
CSE 276E - Robotic System Design & Implementation with Prof. Steven Swanson
Description: End-to-end system design of embedded electronic systems including PCB design and fabrication, software control system development, and system integration. The course is an intensive capstone project course that provides students a chance to integrate knowledge from a wide range of domains into a single project.
Required Knowledge: Basic electronics and intermediate C/C++ programming.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Read through an undergraduate circuits textbook. If you don’t know how to program, you should not take the class.
Link to Past Course: https://sites.google.com/a/eng.ucsd.edu/quadcopterclass/
CSE 283 - Genomics/proteomics/netwk biol with Prof. Vineet Bafna
Description: The development of omics technologies provides us with an unprecedented opportunity to understand cellular function, and higher levels of organization. In this class, we will focus on big data being generated in biology, and the techniques used to analyze the data.
Required Knowledge: While the course should be self-contained, we assume some mathematical maturity. Students who have taken CSE202, CSE282 will find the material more accessible.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Online class on Bioinformatics algorithms
Link to Past Course: http://proteomics.ucsd.edu/vbafna/teaching-2/data-wrangling-in-bix/
CSE 284 - Personal Genomics/Bioinformatics with Prof. Melissa Gymrek
Description: Genome-sequencing is quickly becoming a commodity, and more than a million people have already analyzed their own genomes through direct-to-consumer companies. This course provides an introduction to current bioinformatics techniques for analyzing and interpreting human genomes. We will learn how to interpret a single genome in the context of an entire population. Topics covered include an introduction to human medical and population genetics, human ancestry, finding and interpreting disease-causing variants, genome-wide association studies, genetic risk prediction, analyzing next-generation sequencing data, and how to scale current genomics techniques to analyze hundreds of thousands of genomes. We will also discuss the social impact of the personal genomics revolution. This course is designed for students with a quantitative background and some programming experience who are interested in human genetics and bioinformatics research.
Required Knowledge: Basic UNIX skills, Python programming experience, interest in human genetics
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: CSE 185
Link to Past Course: http://gymreklab.com/teaching/personal_genomics/personal_genomics_2017.html
CSE 291 (A00) - Advanced Statistical Natural Language Processing with Prof. Taylor Berg-Kirkpatrick
Description: This course will explore current statistical techniques for the automatic analysis of natural (human) language data. The dominant modeling paradigm is corpus-driven statistical learning, with a split focus between supervised and unsupervised methods. Advanced Statistical NLP is a lab-based course. Instead of homework assignments and exams, you will complete four hands-on coding projects. We will also consider practical constraints that affect the scalability of real-world NLP systems to big data.
Required Knowledge: This course assumes a strong background in probability, some background in probabilistic graphic models, and a strong ability to program in Java. Prior experience with linguistics or natural languages is helpful, but not required. There will be a lot of statistics, algorithms, and coding in this class. Recommended perquisites are courses on discrete mathematics, probability, statistics, probabilistic graphical models, algorithms, and linear algebra.
Enforced Prerequisite: None
Recommended Preparation for Those Without Required Knowledge: Review probability theory (e.g. conditional independence, probabilistic graphical models) and practice dynamic programming (e.g. Viterbi algorithm) in a language like C++ or Java.
Link to Past Course: http://www.cs.cmu.edu/~tbergkir/11711fa17/
CSE 291 (B00) - Topics in Trustworthy Machine Learning with Prof. Kamalika Chaudhuri
Description: This is a PhD level course on emerging topics in trustworthy machine learning. Traditionally machine learning research has focussed largely on designing methods that have high classification accuracy; however, putting these methods into practice requires attention to other aspects -- such as robustness, interpretability, privacy and fairness -- so that the results obtained can be trusted. This class will look at some of these aspects -- in particular, robustness and causality. If time permits, we will also look at privacy.
Required Knowledge: One or more graduate-level machine learning classes -- CSE 250A/B/C. Graduate-level probability.
Enforced Prerequisite: None
Recommended Preparation for Those Without Required Knowledge: Take the recommended classes in Required Knowledge.
Link to Past Course: N/A
CSE 291 (C00) - Topics on Numerical Methods for Engineering with Prof. CK Cheng
Description: The class covers topics on numerical methods for engineering. We model the system in high dimensional space with temporal behavior. The techniques of matrix solvers, matrix functions, and parallel processing will be discussed. The numerical issues for approximation such as stiffness, stability, and convergence will be reviewed. Students are encouraged to carry out the state of the art projects with team members.
Required Knowledge: Linear algebra
Enforced Prerequisite: Yes. CSE203B, ECE273 or MATH 245A
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: http://cseweb.ucsd.edu/classes/sp14/cse291-b/
CSE 291 (D00) - Unsupervised Learning with Prof. Sanjoy Dasgupta
Description: A broad overview of unsupervised learning, with emphasis on basic principles and on algorithmic and statistical guarantees.
Required Knowledge: Basic coursework in machine learning (at the level of CSE 250B); algorithms (at the level of CSE 202); probability and statistics (at the level of CSE 103); and linear algebra (at the level of Math 18).
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Consult materials for the courses listed above.
Link to Past Course: N/A
CSE 291 (E00) - Deep Learning for Sequences with Prof. David Kriegman
Description: Deep learning has had an enormous impact and has been especially effective for images (ConvNets) and sequences (RNN’s). This course focusses on deep learning for sequence processing and their application including video understanding, language translation, speech recognition, image captioning, OCR, and financial market prediction. After creating a foundation in common architectures (RNN, LSTM, GRU, Attention) and their training, we will dig into recently published papers that highlight methods and applications of deep learning for sequence processing. Each paper will be presented by a student, and all students are expected to participate in a critical discussion of these papers. There will be one or two programming assignments and a full quarter project (individual or small group).
Required Knowledge: Students should have sufficient experience in deep learning to both present papers and engage in critical discussion; they should have experience with deep learning frameworks to implement a substantial full quarter project. There are many ways to have acquired this background including, but not limited to courses such as CSE 253, COGS 181, etc
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: https://paper.dropbox.com/doc/CSE291G-2019-Syllabus-Deep-Learning-for-Sequences--As9DXJb3Vv7TPEIC9wkz8paUAg-wmrBlSsIMnBsZt5kOFHcj
CSE 291 (F00) - Computational Photography with Prof. Ben Ochoa
Description: Computational photography overcomes the limitations of traditional photography using computational techniques from image processing, computer vision, and computer graphics. This course provides a comprehensive introduction to computational photography and the practical techniques used to overcome traditional photography limitations (e.g., image resolution, dynamic range, and defocus and motion blur) and those used to produce images (and more) that are not possible with traditional photography (e.g., computational illumination and novel optical elements such as those used in light field cameras). Upon completion of this course, students will have an understanding of both traditional and computational photography.
Required Knowledge: Linear algebra, calculus, and optimization. MATLAB, Python, or other programming experience.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Undergraduate courses and textbooks on image processing, computer vision, and computer graphics, and their prerequisites.
CSE 291 (G00) - Deep Learning for Semantics, Geometry, and Physics in Robotics with Prof. Hao Su
Description: We will study the latest progress of machine learning methods for semantic understanding, geometry reconstruction and processing, and physics simulation. The course is project-based and will include lecturer presentation and student presentation of fundamental knowledge and recent papers.
Required Knowledge: Machine Learning, Deep Learning, Computer Vision, Geometry, and Physics
Enforced Prerequisite: Yes. WI20 CSE 291 (F00), WI19 CSE 291 (C00), or instructor consent
Recommended Preparation for Those Without Required Knowledge: Go through CSE291-F in Winter 2020.
Link to Past Course: N/A
CSE 291 (H00) - Physics Simulation with Prof. Steven Rotenberg
Description: This course will focus on computational physics simulation with an emphasis on the dynamics of motion. Specific sub-topics include rigid body dynamics, deformable body dynamics, contact, fracture, and fluid dynamics. Additional topics may include galactic, molecular, and vehicle dynamics.
Required Knowledge: Familiarity with Newtonian mechanics. Familiarity with C++ or similar programming language. Familiarity with OpenGL or similar graphics API.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Gain familiarity with the topics listed above.
Link to Past Course: https://cseweb.ucsd.edu/classes/sp19/cse291-d/
CSE 291 (I00) - Applied Cryptography with Prof. Nadia Heninger
Description: This course will cover topics in applied cryptography, with a focus on cryptography in the real world. This quarter we will plan to focus on practical cryptanalysis. There will be implementation exercises and a project.
Required Knowledge: The main prerequisite is mathematical maturity. Algorithms, some algebra, basic number theory, and probability will be useful. Students coming from mathematics should be comfortable with programming.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: N/A