Probabilistic and Bayesian Deep Learning

Update!

If you’re interested in taking CS 345 in Spring 2027, please fill out this form form before October 30th at midnight for optimal consideration. If you missed the deadline, please go ahead and fill out the form anyways and you will be added to the waitlist!

Notice!

This course is under construction.

Update

Looking to learn more about the design of this course? Check out our recent paper about it!

Instructor: Yaniv Yacoby (he/they)

Semester: Spring 2027

Course Number: CS 345 @

Description: In this course, we will incorporate ideas from modern deep learning into the probabilistic framework of machine learning to explore uncertainty, interpretability, and decision-making. The course develops rigorous understanding and practical fluency with Bayesian neural networks, Gaussian Processes, deep generative models (e.g. Variational Autoencoders), and scalable inference methods for high-dimensional, complex models (including variational inference and Markov Chain Monte Carlo methods). A central focus is on how probabilistic thinking informs model design, evaluation, and deployment in real-world, high-stakes settings such as healthcare. Throughout the course, students will implement and experiment with state-of-the-art probabilistic deep learning models in NumPyro, critically analyze their behavior and limitations in a way that bridges theory, computation, and ethical reflection.

Textbook: We wrote this textbook especially for this course.

Approach: In this course, we take a “framework-focused” approach to connect theory to application to ethics—an approach we developed in this paper.

Meeting Times: TBD

Location: TBD

Distributions: Data Literacy, and Mathematical Modeling and Problem Solving.

Prerequisites: CS 245 and permission of the instructor.