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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 245.

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Degree Requirements: DL - Data Literacy (Formerly QRDL)

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Fall

Notes: This cou