Course Syllabus
CS275P: Graphical Models & Statistical Learning
University of California, Irvine, Spring 2023
Prof. Erik Sudderth
CS275P explores methods for statistical machine learning with probabilistic graphical 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, and Bayesian deep learning frameworks for integrating graphical models with deep neural networks. Methods will be motivated by applications including image and video analysis, text and language processing, sensor networks, autonomous robotics, computational biology, and social networks. We explore these applications via homework assignments and a final project.
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
- Textbook: Bayesian Reasoning and Machine Learning, David Barber, Cambridge University Press, 2012.
- Lectures: Mondays and Wednesdays from 3:30-4:50pm in SSPA 1100.
- Discussions: Fridays from 5:00-5:50pm in SSPA 1100, led by the Teaching Assistant.
- Discussions will be in person and also over zoom (https://uci.zoom.us/j/93018839491)
- Instructor: Prof. Erik Sudderth. Office hours Tuesdays from 3:30-4:30pm, DBH 4206.
- Teaching Assistant: Ali Younis
- Mondays 12pm-1pm (Virtual): https://uci.zoom.us/j/93018839491
- Thursdays 12:30pm - 1:30pm (In-Person): DBH 4241
- 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.
Course Summary:
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