Updated July 1, 2020
This page serves the purpose to help graduate students understand each graduate course offered during the 2020-2021 academic year. Please use this page as a guideline to help decide what courses to take.
Fall 2020 Graduate Course Information
CSE 200 - Computability and Complexity with Prof. Mihir Bellare
Description: This class is an introduction to computational complexity theory. This area aims to understand the computational resources required to solve various 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 computationally "easy" or "hard", study relations between easy and hard problems and reductions between them. We study different complexity classes that model different computational capabilities, and study the relations between these.
Required Knowledge: CSE 20, 21, 101, 105 or equivalent classes
Enforced Prerequisite: Yes. CSE 21, CSE 101, and CSE 105, or their equivalents.
Recommended Preparation for Those Without Required Knowledge: Standard textbooks on discrete math, algorithms and computability.
Link to Past Course: http://cseweb.ucsd.edu/classes/wi18/cse200-a/
CSE 202 - Algorithm Design and Analysis with Prof. Russell Impagliazzo
Description: This course is intended primarily for non-theory computer science graduate students who will need to formalize computational problems, and design and implement or use existing efficient algorithms for these problems. The emphasis is on methods that can be used for a wide variety of cutting edge applications, as in genomics, data mining, or VLSI design. This course presents general techniques and paradigms for designing and analyzing algorithms for a variety of computational problems. Techniques include reductions between problems, graph search, greedy algorithms, divide and conquer, backtracking, dynamic programming, and hill-climbing. For each technique, we will see a variety of applications of the technique, starting with relatively straight-forward applications and then going on to look at applications or settings that stretch the uses of the technique. These examples will be presented in class, in independent reading in the text, and discovered by students in assignments. A course project will be on formalizing computational problems and inventing algorithms for them.
Required Knowledge: We assume some undergraduate exposure to discrete mathematics, and to algorithms and their analysis, and the ability to read, recognize, and write valid proofs. For example, CSE 20, CSE 21, and CSE 101 cover the prerequisite material. We will cover many of the same topics from undergraduate algorithms courses. However, after a quick review of the basics, we will move on to related advanced material for each topic. 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. Students without prerequisites will be admitted at their own risk.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: See above
Link to Past Course: https://cseweb.ucsd.edu/classes/wi14/cse202-a/
CSE 210 - Principles of Software Engineering with Prof. Thomas Powell
Description: Building a successful software system is an intellectually challenging activity that is more likely to fail than succeed. Despite a seeming lack of engineering predictability software is becoming increasingly sophisticated and now permeates every aspect of our lives. In short, software is both becoming harder to build and is even more important than ever that it is built "right."
Building things "right" is not necessarily a settled thought. What "right" is shifts as the methods for creating software and associated practices have changed to keep pace with various changes in technology and industry conditions. This course is an introduction to classical best practices as well as current trends in software engineering practices. Readings and case studies including Mythical Man Month, various works from Steve McConnell, and numerous classic and current articles covering topics ranging from patterns, methods, and team practices will be covered. This course will not only introduce foundational concepts of software engineering but will also provide practical experience for students to develop engineering and collaborative skills as they execute a small team project.
This course is run as a seminar, but this quarter will be performed entirely in a remote fashion. While many course aspects allow asynchronous execution, practically speaking the course requires constant and deep participation including near-daily preparation (with hand-ins) and communication with your peers in paper reviews. The project will have weekly milestones and various ceremonies that adhere to common Agile practices and students will be expected to do this in a remote fashion. Project presentations will be required at the end of the course.
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 218 - Advanced Topics in Software Engineering with Prof. Nadir Weibel
Description: Good software engineering (SE), when applied to the real world, is hard to achieve. The tendency is to focus on developing a rushed prototype, and then get to a product as fast as possible, without having enough time to think about the good SE principles that we have been learning in school.
This course is a Software Engineering in Practice course that teaches students how to maintain good software engineering practices, even when under the constant pressure of weekly deadlines, and the continuous need to demonstrate new weekly features of the system under development.
In CSE 218 we will refresh the basic principles of Agile Software Development, and extend it into Agile Project Management. We will learn to use Agile tools and work in a team that embraces Agile practices, such as delivering weekly results, and move from ideation to design, prototyping, and development, with the ultimate goal of creating a working system by the end of the quarter.
In order to make the course more exciting, we pair the software engineering part of the course with a specific topic related to Ubiquitous Computing. The Ubiquitous Computing setting allows us to integrate and explore affordable sensors and interaction devices (e.g., webcams, mobile phone-based sensors, digital pens, Microsoft SenseCam, Microsoft Kinect, Google glasses, portable eye-tracking, HoloLens, Oculus Rift, etc.) and wireless mobile computing devices (e.g., mobile smartphones, Arduino boards with 802.11b wireless connectivity, etc.) to create boundless opportunities for in-the-world computing applications that can transform our lives.
In Fall 2020 CSE 218 will be a remote class and will focus on human-centered design and agile development of Mobile and Web-based Mixed Reality experiences. We will use a range of mobile devices and investigate how to create AR and VR experiences on smartphones, tablets, and laptops. Students will work in remote distributed teams and will develop MR applications that will be demonstrated in a virtual showcase at the end of the quarter.
Required Knowledge: Required is general knowledge in programming applications specifically using C++/C# and Java, as well as web technology. Basic knowledge of Software Engineering methodologies and techniques are required. Interest and knowledge in research methodologies, as well as being capable to read/understand/discuss scientific literature will be important to progress through the readings.
Moreover, graduate students will be required to lead groups, so initiative and willingness to manage a project is required.
Enforced Prerequisite: Yes. CSE 110 and CSE 100, or their equivalents.
Recommended Preparation for Those Without Required Knowledge: https://www.coursera.org/course/hciucsd, https://www.edx.org/course/software-engineering-introduction-ubcx-softeng1x
Link to Past Course: http://ubicomp.ucsd.edu/cse218
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 its 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 224 - Graduate Networked Systems with Prof. George Porter
Description: This course will provide a broad understanding of exactly how the network infrastructure supports distributed applications ranging from web browsing to cloud computing. Topics covered in the course include sockets programming, data centers, and cloud computing, Remote Procedure Calls, scale-out distributed directories, distributed fault tolerance and state management, indirection, overlay networks, load balancing, security, and virtualization.
Required Knowledge: An understanding of operating systems concepts and concurrency is recommended.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Review an undergraduate textbook for information on mutexes, condition variables, mutual exclusion, context switching, processes and basic OS and compiler concepts.
Link to Past Course: http://cseweb.ucsd.edu/~gmporter/classes/fa19/cse224/
CSE 229A - Tops/Seminar/Computer Systems with Prof. Alex Snoeren
Description: Discussion of timely, ongoing work being conducted in the Systems and Networking Research Group and elsewhere.
Required Knowledge: Actively conducting research with a faculty member in the Systems and Networking Research Group
Enforced Prerequisite: Yes. Enrollment is restricted to students working with a faculty member in the group.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: N/A
CSE 229C - Tops/Seminar/Computer Security with Prof. Nadia Heninger
Description: Discussion on problems of current research interest in computer security. Topics to be presented by faculty and students under faculty direction. Topics vary from quarter to quarter. May be repeated for credit.
Required Knowledge: N/A
Enforced Prerequisite: Yes. Consent of instructor.
Recommended Preparation for Those Without Required Knowledge: N/A
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 237C - Validation and Testing of Embedded Systems with Prof. Ryan Kastner
Description: This class focuses on creating embedded system prototypes using a programmable system-on-chip. The class is graded primarily based on the performance in projects that are spread across the class. The projects require the student to implement a digital signal processing (DSP) core and integrate it into a prototype wireless RF system. Students will learn how to use modern high-level synthesis tools to create different DSP architectures.
Required Knowledge: This class is part of the 237 “Embedded Systems” series. This class stands on its own and does not require knowledge from any of the previous 237 classes. Having some understanding of basic digital signal processing (sampling, filtering, transforms) and wireless systems (modulation, estimation) is useful but not required. Equally helpful is having taken classes in computer architecture (and in particular understanding pipelining, memory hierarchy, and digital design). Again, it will be useful but not required as the class is taught in a way that is self-contained. Do note that most students have a good background in at least one of these areas (DSP or computer architecture); if you do not know either, then you will have to work harder.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: You can start by reading the book! The class is built around this book (and vice-versa) and the projects are related to the chapters in that book. For those who do not know DSP, there are countless resources for this. You can learn the basics of finite impulse response (FIR) filters, discrete Fourier transform, and fast Fourier transforms. These form some of the core computations for the projects. Each of these topics will be described in class, but you would be wise to have at least a decent understanding of how these work. The same is true for computer architecture; there are countless resources for this. You generally want to understand the components of a data path (control flow, data flow) and the tradeoffs in memory design (number of ports, throughput, hierarchy, etc.). We will primarily be using the Xilinx Vivado HLS design tool. There are a number of tutorials on this tool at the Xilinx website. If you want to start using the tool early, contact the instructor.
Link to Past Course: http://kastner.ucsd.edu/ryan/cse237c/
CSE 239A - Topics/Seminar in Databases with Prof. Arun Kumar
Description: Weekly seminar on the cutting edge of database research and practice.
Required Knowledge: N/A. It will help if you know what databases are.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Take the relevant courses (232A, 232B, 233, 234, 132A, 132B, 132C) if interested in learning more about databases.
Link to Past Course: Active seminar webpage: https://dbucsd.github.io/seminar
CSE 240A - Princ/Computer Architecture with Prof. Hadi Esmaeilzadeh
Description: This course will cover fundamental concepts in computer architecture. Topics include instruction set architecture, pipelining, pipeline hazards, bypassing, dynamic scheduling, branch prediction, superscalar issue, memory-hierarchy design, advanced cache architectures, and multiprocessor architecture issues.
Required Knowledge: An undergrad computer architecture course
Enforced Prerequisite: None.
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: https://cseweb.ucsd.edu/classes/fa17/cse240A-a/
CSE 243A - Intr Synthesis Method VLSI CAD with Prof. Alex Orailoglu
Description: This course will provide an introduction to the main synthesis methodologies in hardware design, primarily, logic synthesis, and architectural synthesis.
Required Knowledge: Undergraduate level knowledge in digital design and Computer Architecture
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Please review Undergraduate level knowledge in digital design and Computer Architecture
Link to Past Course: http://cseweb.ucsd.edu/classes/fa18/cse243A-a/
CSE 249B - Topics/Seminar in VLSI with Prof. Andrew Kahng
Description: CSE 249B, Fall 2020, will focus on machine learning applications in IC physical design.
The goal is for each student to identify and execute a project that achieves a "project writeup that is submittable to a high-quality conference", by the end of the quarter. Project topics may be pursued in collaboration with others in the class. Instructor approval at the outset, and fairly continuous interactions, are expected. Students should have done well in CSE 241A / ECE 260B or otherwise be familiar with IC implementation (e.g., in an industrial IC design organization). Background and interest in the application of machine learning to areas wherein success depends heavily on domain knowledge, and where data is "small and expensive", are also required.
We will meet weekly Wednesdays 11:00am - 12:20pm (Pacific Time) by Zoom at a channel to which all enrolled students are invited. Students who are overseas should email Prof. Kahng to develop mechanisms by which course participation becomes viable.
Required Knowledge: Students should be familiar with IC physical implementation. Coursework such as CSE 250A, 250B or 253 is a plus. Familiarity with combinatorial algorithms is a plus. Students should have interest in the application of machine learning to areas wherein success depends heavily on domain knowledge, and where data is "small and expensive".
Enforced Prerequisite: Yes. Consent of instructor.
Recommended Preparation for Those Without Required Knowledge: Students who do not have the recommended preparation should contact the instructor well in advance of the quarter to obtain a reading list and some practice exercises.
Link to Past Course: N/A
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/fa19/cse250A-a/
CSE 252A - Computer Vision I with Prof. David Kriegman
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. Will include both classical methods and those based on deep learning.
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/fa18/cse252A-a/
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/fa19/cse258-a/
CSE 260 - Parallel Computation with Prof. Bryan Chin
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: Yes. 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/cse260-winter-2020/home
CSE 274 - Selected Topics in Graphics (Discrete Differential Geometry) with Prof. Albert Chern
Description: The course provides an introduction to discrete differential geometry and its applications in geometry modeling and analysis. The contents include the smooth and discrete theory of curves, surfaces, exterior calculus, the Hodge theory, and the vector bundle theory. The theories are explained alongside with applications including numerical methods for differential equations on manifolds, surface texturing, shape analysis and vector field designs. The course also covers the basics of the graphics software Houdini FX.
Required Knowledge: Linear algebra, multivariable calculus. CSE 176 is recommended.
Enforced Prerequisite: None, but be aware of the required knowledge information.
Recommended Preparation for Those Without Required Knowledge: The course content will be similar to the Caltech/CMU course https://www.cs.cmu.edu/~kmcrane/Projects/DDG/ by Keenan Crane.
Link to Past Course: https://www.cs.cmu.edu/~kmcrane/Projects/DDG/
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-19/
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: Since this is a graduate course we are typically quite loose with prerequisites.
Enforced Prerequisite: None.
Recommended Preparation for Those Without 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 Matlab as part of the class.
Link to Past Course: http://www.hichristensen.com/CSE276C-19/
CSE 290 (C00) - Automation and Algorithms Seminar with Prof. Sean Gao
Description: Students will present recent development in the algorithms for automation in a broad range of domains such as robotics, computer systems, and scientific discovery.
Required Knowledge: Familiarity with search/learning/optimization algorithms and knowledge/interest in a relevant engineering/scientific domain. Have relevant research work to present.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: N/A
CSE 290 (G00) - Data Center Reliability & Security Management with Prof. YY Zhou
Description: We will read and discuss very recent research papers and topics related to data center reliability and security, and identify new research ideas.
Required Knowledge: N/A
Enforced Prerequisite: Yes. Consent of instructor.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: N/A
CSE 290 (H00) - Pixel Cafe with Prof. Ravi Ramamoorthi
Description: Seminar covering latest research in visual computing (computer vision and graphics). Speakers include faculty, students within the center for visual computing, and external speakers.
Required Knowledge: N/A
Enforced Prerequisite: Yes. Consent of instructor.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: N/A
CSE 290 (I00) - Learning for Interaction Seminar with Prof. Hao Su
Description: This seminar is for students and visitors to present research progress on using machine learning methods to integrate perception, planning, and control for building autonomous agents that can intelligently interact with the physical world. Research problems include learning principles such as reinforcement learning, imitation learning, and continual learning, and their applications for learning-based geometry/dynamics modeling, multi-body interaction, robot simulator construction, object manipulation, etc.
Required Knowledge: N/A
Enforced Prerequisite: Yes. Consent of instructor.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: N/A
CSE 291 (A00) - Advanced Data-Driven Text Mining with Prof. Jingbo Shang
Description: Unsupervised, weakly supervised, and distantly supervised methods for text mining problems, including information retrieval, open-domain information extraction, text summarization (both extractive and generative), and knowledge graph construction. Bootstrapping, comparative analysis, and learning from seed words and existing knowledge bases will be the key methodologies.
Required Knowledge: Knowledge about Machine Learning and Data Mining; Comfortable coding using Python, C/C++, or Java; Math and Stat skills.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Intro-level AI, ML, Data Mining courses.
Link to Past Course: None, this is the first offering.
CSE 291 (C00) - Probabilistic Approaches to Unsupervised Learning with Prof. Sanjoy Dasgupta
Description: This course is a rigorous treatment of latent variable models and other probabilistic approaches to unsupervised learning. The models we'll cover include exponential families, mixture models, hidden Markov models, Dirichlet processes, topic models, and variational autoencoders. The class will emphasize algorithms with provable guarantees for sampling, inference, and learning.
Required Knowledge: Familiarity with the basics of linear algebra, probability and statistics, and algorithms.
Enforced Prerequisite: None
Recommended Preparation for Those Without Required Knowledge: Course will not be manageable without acquiring this background.
Link to Past Course: None, this is the first offering of the course.
CSE 291 (D00) - Data Systems for Machine Learning 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 with quizzes, exams, and paper reading components for evaluation. It is primarily tailored for MS students interested in the state of the art of systems for scalable data science and ML engineering.
Required Knowledge: A course on ML 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, but no specific course numbers are enforced. 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.
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/wi19/cse291-f/
CSE 291 (E00) - Applied Cryptography with Prof. Nadia Heninger
Description: This is a course on applied cryptography, with a significant focus on cryptanalysis. Topics to be covered include random number generation, symmetric cryptography, stream ciphers, block ciphers, hash functions, modes of operation, public-key cryptography and cryptanalysis, RSA, Diffie-Hellman, DSA, elliptic curve cryptography, algorithmic techniques in cryptanalysis, secure channels, TLS, and cryptography in practice.
Required Knowledge: Mathematical maturity
Enforced Prerequisite: Yes. CSE 202 or consent of instructor.
Recommended Preparation for Those Without Required Knowledge: N/A
Link to Past Course: https://cseweb.ucsd.edu/classes/sp20/cse291-i/
CSE 291 (F00) - Algorithms for Big Data with Prof. Cyrus Rashtchian
Description: Many data-driven areas (computer vision, AR/VR, recommender systems, computational biology) rely on probabilistic and approximation algorithms to overcome the burden of massive datasets. This course explores a foundational view of these techniques by analyzing them through a geometric lens. The first two weeks will review linear algebra (normed spaces, orthogonality, random matrices) and randomized algorithms (approximation guarantees, concentration inequalities). Then, we dive into designing and analyzing algorithms for big data. The main topics include: sampling/sketching, dimensionality reduction, clustering, nearest neighbor search, and distributed models. Throughout the course, we will discuss motivating applications and current research trends, such as adversarial robustness, explainable AI, and learned embeddings.
Required Knowledge: Intermediate understanding of algorithms, linear algebra, and probability theory. Mathematical maturity is a must: the class is based on theoretical ideas, and you are expected to be able to read and write formal mathematical proofs. Exposure to graduate-level randomized algorithms is a plus.
Enforced Prerequisite: None.
Recommended Preparation for Those Without Required Knowledge: Review algorithmic ideas (data structures, hashing, binary search), linear algebra fundamentals (Euclidean distance, Manhattan distance, inner products, cosine similarity), and probability fundamentals (expectation, variance, independence, Markov's inequality).
Link to Past Course: http://madscience.ucsd.edu/cse291.html
CSE 291 (G00) - Deep Generative Models with Prof. Rose Yu
Description: Deep generative models combine the generality of probabilistic reasoning with the scalability of deep learning. This research area is at the forefront of deep learning and has given state-of-the-art results in text generation, video synthesis, molecular design, amongst many others. This course will cover recent advances in deep generative models, including variational autoencoders, generative adversarial networks, autoregressive models, and normalizing flow models. This is a Ph.D. level course with emphasis on mathematical principles as well as practical know-how. The course will be a combination of lectures, student presentations, and team projects.
Required Knowledge: Familiarity with statistical inference and deep learning.
Enforced Prerequisite: Yes. CSE 250A or its equivalent.
Recommended Preparation for Those Without Required Knowledge: CSE 253 Deep Neural Networks or equivalent, experience with deep learning frameworks
Link to Past Course: N/A, first offering