Course Syllabus

CS275P: Machine Learning with Generative Models

University of California, Irvine, Spring 2024

Prof. Erik Sudderth

CS275P explores methods for statistical machine learning with probabilistic generative models. We will study efficient inference algorithms based on optimization-based variational methods, and simulation-based Monte Carlo methods. Several approaches to learning from data will be covered, including conditional models for discriminative learning, Bayesian methods for controlling model complexity, probabilistic graphical models, and deep generative models such as variational autoencoders and diffusion models.  Methods will be motivated by applications including image and video analysis, text and language processing, sensor networks, autonomous robotics, computational biology, and social networks.

Course Materials

• Ed Discussion will be used for all course announcements, discussions, and questions.  All enrolled students should watch for important announcements and post questions (anonymously if you prefer) about course content.
• Lecture Calendar including readings, slides, and how to access lecture streams and recordings
• Discussion Calendar including links to Jupyter notebooks, and how to access discussion recordings
• Homework Assignments are distributed via Canvas, but submitted and graded via gradescope
• Homework Policies and Resources, including the Collaboration and Academic Honesty Policy
• Final Project deadlines and requirements

Course Information

• Textbooks:  Probabilistic Machine Learning: Advanced Topics, Kevin P. Murphy, MIT Press, 2023.
  Bayesian Reasoning and Machine Learning, David Barber, Cambridge University Press, 2012.
• Lectures:  Tuesdays and Thursdays from 12:30-1:50pm in ICS 174.
• Discussions:  Fridays from 10:00-10:50am in DBH 1600, led by the Head Teaching Assistant.
• Instructor:  Prof. Erik Sudderth. 
• Head Teaching Assistant:  Ali Younis
• Teaching Assistant:  Dahai Hao
• Office Hours:
  • Mondays 5:00-6:00pm:  Dahai Hao, ICS 458A.
  • Tuesdays 10:00am-12:00pm:  Ali Younis, DBH 4241.
  • Wednesdays 2:00-3:00pm:  Prof. Sudderth, DBH 4206.
  • Thursdays 11:00am-12:00pm:  Dahai Hao, ICS 458A.
• Grading:  60% homework assignments, 40% final projects.

Course Prerequisites

A previous introductory course in machine learning (UCI CS273P).  Comfort with multivariable calculus, linear algebra, and probability theory.  Python programming, and some experience with optimization algorithms, required for homeworks and projects.