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Full-time Ph. D. Program

Ph.D. Course Guide

Ph.D. Coursework

We keep a complete list of courses that we offer on the “Courses” page of our website.

If you’re looking for the quarters in which we expect to offer specific courses, check out our teaching schedule.

If you’re looking for an add code, check out the 500-level section of our non-major registration instructions.

Degree Requirements

A total of approximately 90 credits is required to complete the CSE Ph.D. program, 60 of which must be earned at the University of Washington. The University requires 60 credits earned before scheduling the General Exam and requires a cumulative GPA of 3.0 for graduation. Coursework taken toward the M.S. degree is applicable toward the Ph.D. degree.

As you plan your coursework, keep in mind the following requirements:

  • 45 credits minimum must be from the Computer Science & Engineering curriculum.
  • 15 credits may be in supporting fields, such as: engineering, mathematics, natural sciences, business administration, linguistics, philosophy, psychology, or medicine.

Of the 90 total credits required for the Ph.D. degree:

  • 40 credits must be in 500 level courses or above.
  • At least 18 numerically graded credits at the 500-level must be completed in order to earn the Master’s Degree
  • 27 credits must be Computer Science 800 (dissertation).

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Upcoming 500-level Course Offerings

The sections below highlight course offerings in the upcoming academic year.

Breadth Courses

Accessible Accordion

Group 1: Theory, Mathematical, & Formal Reasoning

  • CSE 505 – Principles of Programming Languages – Andres Erbsen
  • CSE 515 – Statistical Methods in Computer Science – Sewoong Oh (Also Group 3)
  • CSE 534 – Quantum Information & Computation – Chinmay Nirkhe
  • CSE 535 – Theory of Optimization & Continuous Algorithms – Thomas Rothvoss
  • CSE 546 – Machine Learning – Matt Golub // Pang Wei Koh (Also Group 3)

Group 2: System Design & Implementation

  • CSE 548 – Computer Systems Architecture – Mark Oskin
  • CSE 561 – Computer Communication & Networks – Ratul Mahajan
  • CSE 564 – Computer Security & Privacy – David Kohlbrenner (Also Group 4)
  • CSE 567 – Digital VLSI: Gates to Systems – Michael Taylor

Group 3: ML/AI, Interacting with Data, & Statistical Applications

  • CSE 515 – Statistical Methods in Computer Science – Sewoong Oh (Also Group 1)
  • CSE 541 – Interactive Learning – Kevin Jamieson
  • CSE 546 – Machine Learning – Matt Golub // Pang Wei Koh (Also Group 1)

Group 4: Human-facing

  • CSE 564 – Computer Security & Privacy – David Kohlbrenner (Also Group 2)
  • CSE 580 – Computing for Social Good – Kurtis Heimerl

Group 1: Theory, Mathematical, & Formal Reasoning

  • CSE 546 – Machine Learning – Grover // Natasha Jaques (Also Group 3)

Group 2: System Design & Implementation

  • CSE 549 – High-Performance Computer Architectures – Michael Taylor
  • CSE 550 – Computer Systems – Ratul Mahajan
  • CSE 554 – Systems for Machine Learning – Baris Kasikci // Stephanie Wang
  • CSE 567 – Digital VLSI: Gates to Systems – ECE

Group 3: ML/AI, Interacting with Data, & Statistical Applications

  • CSE 512 – Data Visualization – Leilani Battle (Also Group 4)
  • CSE 527 – Computational Biology – Su-In Lee
  • CSE 542 – Reinforcement Learning – Kevin Jamieson
  • CSE 546 – Machine Learning – Grover // Natasha Jaques (Also Group 1)
  • CSE 571 – AI-based Mobile Robotics – Deiter Fox
  • CSE 582 – Ethics in Artificial Intelligence – Yulia Tsvetkov (Also Group 4)

Group 4: Human-facing

  • CSE 512 – Data Visualization – Leilani Battle (Also Group 3)
  • CSE 582 – Ethics in Artificial Intelligence – Yulia Tsvetkov (Also Group 3)

Group 1: Theory, Mathematical, & Formal Reasoning

  • CSE 507 – Computer-Aided Reasoning for Software – Zachary Tatlock
  • CSE 521 – Design & Analysis of Algorithms I – Shayan Oveis Gharan
  • CSE 525 – Randomized Algorithms & Probabilistic Analysis – Anup Rao
  • CSE 526 – Cryptography – Nirvan Tyagi

Group 2: System Design & Implementation

  • CSE 562 – Mobile Systems & Applications – Shyam Gollakota (Also Group 3)

Group 3: ML/AI, Interacting with Data, & Statistical Applications

  • CSE 547 – Machine Learning for Big Data – STAT
  • CSE 562 – Mobile Systems & Applications – Shyam Gollakota (Also Group 2)
  • CSE 574 – Explainable Artificial Intelligence – Su-In Lee
  • CSE 576 – Computer Vision – Linda Shapiro
  • CSE 579 – Intelligent Control through Learning & Optimization – Abhishek Gupta

Group 4: Human-facing

CSE 599: Special topics

Accessible Accordion

CSE 599 (I): High-Dimensional Expanders & Applications

Description: This will be a graduate course that will cover topics in graph expansion and its generalization to higher dimensions. The second half of the course will focus on recent applications of high-dimensional expanders to long-standing open questions coding theory and probabilistically checkable proofs.

Prerequisites: No prerequisites except for mathematical maturity.


CSE 599 (J): Social Reinforcement Learning

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

TBD

TBD