Course Guide

Course	Instructor	Category
CSE 521 Design and Analysis of Algorithms I 521 Course Pitch	Thomas Rothvoss	Theory, Mathematical, & Formal Reasoning
CSE 527 Computational Biology 527 Course Pitch	Su-In Lee	ML/AI, Interacting with Data, & Statistical Applications
CSE 534 Quantum Information and Computation 534 Course Pitch	Chinmay Nirkhe	Theory, Mathematical, & Formal Reasoning
CSE 546 Machine Learning 546 Course Pitch	Matthew Golub and Pang Wei Koh	Theory, Mathematical, & Formal Reasoning OR ML/AI, Interacting with Data, & Statistical Applications
CSE 543 Deep Learning	Simon Du	ML/AI, Interacting with Data, & Statistical Applications
CSE 548 Computer Systems Architecture	Mark Oski	System Design & Implementation
CSE 557 Computer Graphics	Gilbert Bernstein	ML/AI, Interacting with Data, & Statistical Applications OR Human-facing
CSE 573 Artifical Intelligence 573 Course Pitch	Luke Zettlemoyer	ML/AI/Statistical Applications/Interacting with Data
CSE 579 Intelligent Control through Learning and Optimization	Abhishek Gupta	ML/AI, Interacting with Data, & Statistical Applications
CSE 580 Computing for Social Good	Kurtis Heimerl	Human-facing

Course	Instructor	Category
CSE 503 Software Engineering	Michael Ernst	System Design & Implementation
CSE 510 Human-Computer Interaction 510 Course Pitch	James Fogarty	Human-facing
CSE 517 Natural Language Processing 517 Course Pitch	Noah Smith	ML/AI, Interacting with Data, & Statistical Applications
CSE 546 Machine Learning	Matt Golub, Pang Wei Koh	Theory, Mathematical, & Formal Reasoning OR ML/AI, Interacting with Data, & Statistical Applications
CSE 550 Systems for All	Baris Kasikci	System Design & Implementation
CSE 556 Computational Fabrication 556 Course Pitch	Adriana Schulz	ML/AI, Interacting with Data, & Statistical Applications OR Human-facing
CSE 564 Computer Security And Privacy 564 Course Pitch	Tadayoshi Kohno	System Design & Implementation OR Human-facing
CSE 567 Principles Of Digital Systems Design	Michael Taylor	System Design & Implementation
CSE 582 Ethics in AI	Yulia Tsvetkov	ML/AI, Interacting with Data, & Statistical Applications OR Human-facing

Course	Instructor	Category
CSE 505 Programming Languages CSE 505 Course Pitch	Zachary Tatlock	Theory, Mathematical, & Formal Reasoning
CSE 512 Data Visualization 512 Course Pitch	Jeffrey Heer	ML/AI, Interacting with Data, & Statistical Applications OR Human-facing
CSE 525 Random Algorithms 525 Course Pitch	Shayan Oveis Gharan	Theory, Mathematical, & Formal Reasoning
CSE 526 Cryptography 526 Course Pitch	Stefano Tessaro	Theory, Mathematical, & Formal Reasoning
CSE 541 Interactive Learning 541 Course Pitch	Kevin Jamieson	ML/AI, Interacting with Data, & Statistical Applications
CSE 547 Machine Learning for Big Data 547 Course Pitch	Statistics Department Instructor	ML/AI, Interacting with Data, & Statistical Applications
CSE 562 Mobile & Wireless Systems 562 Course Pitch	Shyam Gollakota	System Design & Implementation OR ML/AI, Interacting with Data, & Statistical Applications
CSE 571 AI-based Mobile Robotics 571 Course Pitch	Dieter Fox	ML/AI, Interacting with Data, & Statistical Applications
CSE 574 Explainable AI	Su-In Lee	ML/AI, Interacting with Data, & Statistical Applications
CSE 581 Computer Ethics 581 Course Pitch	Katharina Reinecke	Human-facing

CSE 599 (I): Topics in Robust Statistics

• Instructor: Jerry Li
• Non-major Enrollment: Process Info || CSE Doctoral Course Enrollment Petition
• Time & Location: M/W || 130-250 || TBD

Description: In the past decade, there has been a tremendous amount of work on developing new, efficient algorithms for statistical inference problems that are robust to potentially chosen outliers. The goal of this class is to develop the statistical and algorithmic theory behind these results, as well as discuss directions for future research.

Prerequisites: Mathematical maturity, familiarity with probability theory, statistics, and linear algebra

CSE 599 (J1): Social Reinforcement Learning

• Instructor: Natasha Jaques
• Non-major Enrollment: Process Info || CSE Doctoral Course Enrollment Petition
• Time & Location: TBA || TBA

Description: How can we accelerate AI when learning in an environment with other intelligent agents? This course focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. After reviewing the basics of deep reinforcement learning (RL), we will cover RL fine-tuning of large language models (LLMs), including RL from human feedback, and multi-turn RL. We will then turn our attention to multi-agent RL, examining the complexities of modeling, learning from, and coordinating with other agents. Topics will include zero-shot coordination with humans, learning from human feedback, and emergent complexity. The course will then link these two perspectives, and show how techniques developed for learning from other agents can provide a new path for training safer and more effective language models.

The course is designed to be a research project course, and help students learn how to successfully complete a research paper on these topics. In addition to reading and discussing relevant research papers, students will submit a team-based final project in the form of a research paper. Although we will cover a brief introduction to reinforcement learning (RL), familiarity with RL and deep learning is encouraged.

Prerequisites: Machine Learning (e.g. CSE 446 or 546) or Deep Learning

CSE 599 (O): Systems for Machine Learning

• Instructor: Stephanie Wang
• Non-major Enrollment: Process Info || CSE Doctoral Course Enrollment Petition
• Time & Location: TBA || TBA

Description: Design and implementation of systems that perform modern machine learning. Languages, compilers, computer architectures, and distributed computing for machine-learning computations, including training and inference. Advanced system techniques to scale modern machine-learning models and optimize computation, memory, and communication.

Prerequisites: CSE 451, CSE 452, CSE 446.

CSE 599 (S): Advanced Machine Learning

• Instructor: Jamie Morgenstern
• Non-major Enrollment: Process Info || CSE Doctoral Course Enrollment Petition
• Time & Location: T/Th || 1130-1250 || JHN 102

Description: The goal of this class is to lay the foundations for graduate research in machine learning, both on the theoretical and empirical side. We will cover fundamentals of generalization theory such as Rademacher complexity and core results in optimization with a focus on first-order methods, building up to widely used variants of stochastic gradient descent. On the empirical side, we will focus on recent advances leveraging large models and datasets.

Prerequisites: Linear algebra (e.g. MATH 208 or MATH 308); probability (e.g. STAT 390); Machine Learning (e.g. CSE 446 or 546). This is co-listed with 493S. Undergrads should sign up for 493S

CSE 599(C): Quantum Learning Theory

• Instructor:Andrea Coladangelo
• Non-major Enrollment:CSE Doctoral Course Enrollment Request
• Time & Location: T/Th; 11:30-12:50; TBA

Description: This course is an introduction to the topic of “quantum learning theory”, in the sense of learning properties of a quantum state given copies of it. Unlike a classical string, whose description is entirely known from reading it once, it is not in general possible to learn the “description” of a quantum state given a single copy of it, since a measurement in general disturbs it. This leads to the question: when can we learn (a useful description of) a quantum state? We will explore various settings starting from foundational results on quantum state “discrimination” (i.e. identifying a state from a known family) and quantum state ”tomography” (i.e. learning the entire description of the state given a large amount of copies), and then moving to recent developments on “shadow tomography” (i.e. learning a useful classical description of a state given relatively few copies). This is an advanced class: having done well in the graduate course “Quantum Information and Computation” (CSE 534) (or having an equivalent background) is a prerequisite.

CSE 599(K): Empirical Research Methods

• Instructor: Rene Just
• Non-major Enrollment: CSE Doctoral Course Enrollment Request
• Time & Location: M/W; 1:30-2:50; ECE 003

Description: Valid experimental designs, sound data analyses, and reproducibility of empirical results are core tenets of the scientific method — crucial not only for specific domains in computer science but rather any field that seeks empirical evidence.

This course covers qualitative and quantitative research methods and focuses on properly designing experiments and observational studies, choosing appropriate statistical methods and models, and reasoning about the validity of experimental designs (in terms of internal, external, and construct validity). This course involves lectures, paper discussions, as well as a hands-on experience for data analysis and visualization with R.

CSE 599(O): Human Robot Interaction

• Instructor:Maya Cakmak
• Non-major Enrollment: Open to all Grad Students Starting Period II; Undergrads please complete CSE Doctoral Course Enrollment Request
• Time & Location: T/Th; 11:30-12:50; TBA

Description: This course introduces students to the research field of Human-Robot Interaction (HRI), which is at the intersection of Robotics and Human-Computer Interaction and brings many interdisciplinary perspectives from psychology, social sciences, artificial intelligence, economics, and ethics, among others. Robots as a technology have a unique value to offer, which is unlike any other computing technology (PCs, smartphones, wearables, AR/VR). That is not only because they can move about the environment and physically interact with it, but also because people treat robots differently — like social agents. It is therefore imperative that robots are designed, built, and programmed taking into account humans that will co-inhabit, interact with, and use them. The course will explore how HRI researchers have ventured to do so for the past few decades, covering topics such as shared autonomy, robot learning from humans, anthropomorphization, perception and modeling of humans, and more. The course will involve interactive lectures, drawing examples from recent publications in the field of HRI, discussions based on weekly readings, and a quarter-long project to get students hands-on experience with HRI research methods.

There are no background prerequisites–any graduate student is welcome to join. Undergraduate students should check with the instructor to get permission to join.

CSE 599 (N): Machine Learning for Neuroscience

• Instructor: Matt Golub
• Non-major Enrollment: CSE Doctoral Course Enrollment Request
• Time & Location: M/W; 1:30-2:50; **

Description: Brains are remarkably complex, massive networks of interconnected neurons that underlie our abilities to intelligently sense, reason, learn, and interact with our world. Technologies for monitoring neural activity in the brain are revealing rich structure within the coordinated activity of these interconnected populations of neurons. In this course, we will discuss machine learning models that can be applied toward 1) understanding how neural activity in the brain gives rise to intelligent behavior and 2) designing algorithms for brain-interfacing biomedical devices. Topics will include basic neurobiology, classical probabilistic machine learning foundations, and modern deep learning approaches, including variational autoencoders and recurrent neural networks. Coursework will include readings from the machine learning and computational neuroscience literature, programming assignments, and a final modeling project applied to neural population data.

 

CSE 599: (H) Advances and Challenges in Language Models, Reasoning, and AI Agents

• Instructor: Hannaneh Hajishirzi
• Non-major Enrollment: CSE Doctoral Course Enrollment Request
• Time & Location: M/w; 3:00-4:20;

Description: TBA

 

CSE 599 (K): LLM Serving Systems

• Instructor: Baris Kasikci
• Non-major Enrollment: CSE Doctoral Course Enrollment Request
• Time & Location: MW; 10:00-11:20; **

Description: This class is about optimizing inference for large language models (LLMs). It delves into the challenges of deploying these models at scale and examines advanced strategies to enhance inference efficiency. Topics include an in-depth analysis of transformer architecture and performance analysis methodologies such as the roofline model . We will study both the attention and feed forward layers in detail alongside key optimizations that have been proposed. Topics include memory management, sparsity-related optimizations, hardware-aware optimizations, and speculative decoding. In addition, we will explore parallelization strategies used in inference systems and learn about collective communications. Finally, we will study scheduling systems that execute serving requests in an optimized way. The class will involve a number of hands-on programming assignments meant to solidify the introduced concepts.

 

CSE 599 (D): Finite Model Theory

• Instructor: Dan Suciuo
• Non-major Enrollment: CSE Doctoral Course Enrollment Request
• Time & Location: M/W; 10:00-11:20; **

Description: Finite Model Theory studies logic when the model is restricted to be finite. Applications of finite model theory include verification, databases, and programming languages. The topics covered range from classical to more recent results, such as: 0/1 laws, theory of conjunctive queries, descriptive complexity (connection between the logic language and complexity classes), pebble games, the AGM bound, deriving algorithms from information inequalities. The course meets twice per week, and there will be a few homework exercises. There is no exam.

 

CSE 599 (S): Advanced Machine Learning

• Instructor: Sewoong Oh
• Non-major Enrollment: CSE Doctoral Course Enrollment Request
• Time & Location: T/Th; 10:00-11:20; CSE2 G10

Description: The goal of this class is to lay the foundations for graduate research in machine learning, both on the theoretical and empirical side. We will cover fundamentals of generalization theory such as Rademacher complexity and core results in optimization with a focus on first-order methods, building up to widely used variants of stochastic gradient descent. On the empirical side, we will focus on recent advances leveraging large models and datasets.

 

CSE 599 (L): Mobile & Agentic AI Systems (AKA CSE 562)

• Instructor: Shyam Gollakota
• Non-major Enrollment: CSE Doctoral Course Enrollment Request
• Time & Location: M/W; 1:30-2:50; **

Description: Ready to build intelligent systems that think and act independently? Mobile and Agentic AI Systems is a dynamic, project-driven course where you’ll learn how to design and
develop agentic AI systems, smart technologies capable of sensing, reasoning, and making autonomous decisions. We’ll dive into building AI agents that operate on mobile platforms, leveraging sensors and edge
computing to create adaptive and proactive applications. Through hands-on projects, you’ll explore how to construct systems that interact seamlessly with their environment, from smart assistants, agents
systems to context-aware apps. If you’re excited about pushing the boundaries of AI by creating systems that can learn, adapt, and act with autonomy, this course will equip you with the skills and tools to lead
the way.

 

CSE 599 (G): Deep Learning for Computer Vision

• Instructor: Ranjay Krishna
• Non-major Enrollment: CSE Doctoral Course Enrollment Request
• Time & Location: T/Th; 10:00-12:20; CSE2 G20;

Description: Deep Learning has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection and language understanding tasks like summarization, text generation and reasoning. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art systems.

This course is a deep dive into the details of deep learning algorithms, architectures, tasks, metrics, with a focus on learning end-to-end models. We will begin by grounding deep learning advancements particularly for the task of image classification; later, we will generalize these ideas to many other tasks. During the 10-week course, students will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in deep learning. Additionally, the final assignment will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. Through multiple hands-on assignments and the final course project, students will acquire the tool set for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks.

CSE 590 (C): Computational Biology

• Instructor:Su-In Lee (she/her)
• 590C Course Website

Description: A taste of current research in Computational Biology (local and non-) + critical reading of literature + presentation skills. Students, with faculty advice, pick and present CompBio papers from recent journals/conferences. Students & faculty also present their own research (mostly in Spring, but may be sprinkled throughout, depending on schedules). Background knowledge of biology is not assumed; come learn!

Non-major Enrollment: The seminar is interdisciplinary, and non-majors are welcome. Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.

Mailing List: See Webpage

 

 

CSE 590 (C1): Change Seminar

• Instructor:Kurtis Heimerl (he/him)
• 590C1 Course Website

Description: Change is a group of faculty, students, and staff at the UW who are exploring the role of information and communication technologies (ICT) in improving the lives of underserved populations, particularly in the developing world (though domestically as well). We cover topics such as global health, education, micro finance, agricultural development, and general communication, and look at how technology can be used to improve each of these areas.

Non-major Enrollment: The seminar is interdisciplinary, and non-majors are welcome. Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.

Mailing List: http://changemm.cs.washington.edu/mailman/listinfo/change

 

 

CSE 590 (D): Creativity and Computing

• Instructor:Steve Tanimoto (he/him)
• 590D Course Website

Description: Computer scientists ask what it means for a work or process to be “creative” seriously, seeking formal models and tools to help humans and computers define, explore, and augment solution spaces together. We will cover foundational work on computational approaches to creativity as well as modern applications of this work to fields including the visual arts, music, mathematics, and the sciences.

The seminar will consist of weekly discussions of readings that help us understand creative processes more formally and computationally. Participants are asked to take one hour a week for reading preparation, and to co-lead one discussion. We have background readings prepared, and are looking forward to selecting additional readings based on everyone’s research and hobbies. It should be a fun time!

Non-major Enrollment: The seminar is available for all UW students and the content is designed to be widely accessible. Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.

Mailing List: N/A

 

CSE 590 (E): Computer Science Education Seminar

• Instructor:Ruth Anderson (she/her)
• Course Website: TBA

Description: Are you interested in discussing different approaches to teaching Computer Science? Are you wondering what kind of research people do in CS education? Are you thinking about a career that involves a lot of CS teaching?<br.
A seminar for people interested in discussing topics related to Computer Science education. The format for this quarter will be a weekly discussion of readings from a variety of sources such as CS education conferences (e.g. SIGCSE, ITiCSE, ICER), journal articles on teaching approaches, or excerpts from books on teaching. Participants will be expected to do the readings, participate in weekly discussions, and co-lead one of the discussions.

Non-major Enrollment:Complete the 590E enrollment request form.

 

 

CSE 590 (F): Computing And The Developing World

• Instructor:Richard Anderson (he/him)
• Course Website: TBA

Description: The seminar will consist of weekly discussions of readings that help us understand creative processes more formally and computationally. Participants are asked to take one hour a week for reading preparation, and to co-lead one discussion. We have background readings prepared, and are looking forward to selecting additional readings based on everyone’s research and hobbies. It should be a fun time!

• Non-major Enrollment: Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.

 

 

CSE 590 (H): HCI/Interactive System Seminar

• Instructor:James Fogarty (he/him)
• Course Website: TBA

Description: A weekly seminar held on Fridays at noon, run by HCI PhD students, where we gather informally to discuss new and foundational HCI literature, get to know one another, and learn together. Typically, it’s a mixture of guest speakers, paper discussions, and informal conversations about HCI research. HCI Seminar is intended primarily for graduate students who do HCI research with CSE faculty members.

Non-major Enrollment: Open to students who are research-active with relevant CSE faculty. Email grad-advising@cs.washington.edu with your request, specifying who you are research-active with, in order to receive an add code.

 

 

CSE 590 (J): Dub Seminar

• Instructor:James Fogarty (he/him)
• Course Website: TBA

Description: DUB is a grassroots alliance of faculty, students, researchers, and industry partners interested in Human Computer Interaction & Design at the University of Washington.

Our mission is to bring together an interdisciplinary group of people to share ideas, collaborate on research, and advance teaching related to the interaction between design, people, and technology.

Non-major Enrollment: Student researchers can email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.

Mailing List: Dubs Mailing List

 

 

CSE 590 (L): Networks

• Instructor:Tom Anderson ()
• Course Website: TBA

Description: TBA

 

CSE 590 (N): Software Engineering

• Instructor:Rene Just (he/him)
• Course Website: TBA

Description: TBA

Non-major Enrollment: Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.

 

 

CSE 590 (P): Programming Languages

• Instructors:Zack Tatlock (he/him), Gilbert Bernstein (he/him),
• Course Website: TBA

Description: TBA

Non-major Enrollment: Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.

 

 

CSE 590 (Q): Database Seminar

• Instructor:Magdalena Balazinska (she/her)
• Course Website: TBA

Description: Weekly seminar organized by database faculty and students where we read papers on exciting topics related to data management.

Non-major Enrollment: The seminar is available for all UW students and the content is designed to be widely accessible. Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.

Mailing List: data-science-seminars@cs.washington.edu

 

 

CSE 590 (R): Robotics Colloquium

• Instructor:Maya Cakmak (she/her)
• 590R Course Website

Description:

The Robotics Colloquium features talks by invited and local researchers on all aspects of robotics, including control, perception, machine learning, mechanical design, and interaction. The colloquium is held Fridays between 1:30-2:30pm.

Non-major Enrollment: Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.

 

CSE 590 (V): Vision

• Instructor:Linda Shapiro (she/her)
• Course Website: TBA

Description: Only for Linda Shapiro’s research students in CSE, ECE and BIME.

Non-major Enrollment: Only students who are doing research with Linda Shapiro.

 

 

CSE 590 (W): Create Accessibility Seminar

• Instructor:Jennifer Mankoff (she/her)
• 590W Course Website

Description: The seminar is for students and faculty members to explore research in accessible computing for people with disabilities in the context of human-computer interaction (HCI). The seminar consists of short student presentations of current research results, followed by discussion and critical evaluations the research.

 

 

CSE 590 (X):How to PhD II

• Instructor:Anna Karlin (she/her)
• Course Website: N/A

Description: A seminar for first year PhD students focused on enriching students’ sense of belonging, camaraderie and purpose in their first year in the program. The seminar also addresses advising, choosing research problems, and the core skills needed to thrive in the Ph.D. program and beyond, including making engaging presentations and writing clearly, time management and work-life balance. Meets 3-4 times per quarter.

 

 

CSE 590 (Y): Computer Security And Privacy Seminar

• Instructors:Franziska Roesner (she/her), Tadayoshi Kohno (he/him), David Kohlbrenner (he/him)
• 590Y Course Website

Description: The focus of this quarter is on papers (to be selected by participants) appearing in recent computer security & privacy or security & privacy-adjacent venues. All enrolled participants are expected to present at least one paper and to attend the rest of the presentations.

Non-major Enrollment: Email instructor for permission and forward confirmation to grad-advising@cs.washington.edu in order to receive an add code.

Mailing List: https://mailman.cs.washington.edu/mailman/listinfo/uw-security-research

 

 

CSE 590 (Z): Theory

• Instructor:James R Lee (he/him)
• Course Website: TBA

Description:

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CSE 590 (A): Programming Systems

• Instructor: Dan Grossma (he/him)
• Course Website: TBA

Description: TBA

 

CSE 590 (B): TBA

• Instructor: Rajesh Rao (he/him)
• Course Website: TBA

Description: TBA

 

CSE 590 (D): Database

• Instructor: Dan Suciu
• Course Website: TBA

Description: TBA

 

CSE 590 (O): TBA

• Instructor: Luis Ceze
• Course Website: TBA

Description: TBA

 

CSE 590 (P): TBA

• Instructor: Linda Shapiro
• Course Website: TBA

Description:

 

CSE 590 (Q): TBA

• Instructor: Luke Zettlemoyer
• Course Website: TBA

Description: TBA

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