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Probabilistic Graphical Models (CS 598 OOK) Spring 2020

What Where
Time/place TR 2:00pm-3:15pm 0216 Siebel / Catalog
Class URL
Recorded Lecture, Instructions
Transcribed Lecture
Web forum Piazza » (sign up!)
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Course Staff


Sanmi Koyejo

Office: 3314 SC

Office hours: Tues 3:30-4:30pm; or by appointment.

E-mail: sanmi

Teaching Assistants:

name contact ( office hours where
Sahand Mozaffari sahandm2 12:00pm-2:00pm Fridays On the whiteboard by SC 3407 (Tentative)

Course Description

Probabilistic Graphical Models are efficient representations of joint distributions using graphs, with a range of applications to machine learning, computer vision, natural language processing and computational biology, among other fields. The course will cover the fundamentals of probabilistic graphical models, including techniques for inferring properties of the distribution given the graph structure and parameters, and for learning the graphical model from data. The course will also cover selected special topics such as the basics of causality, approximate inference, Bayesian deep neural networks, and structure learning for graphical models.

Topics to be covered


Students are expected to have taken a class in linear algebra, probability and statistics and a basic class in theory of computation and algorithms. Students are expected to be familiar with the python programming language.

Course Materials

Required Text

There is no required textbook for the course. But Michael Jordan's draft of "An Introduction To Probabilistic Graphical Models" (available online) is highly recommended.

Optional books with relevant material:

Lecture slides, course handouts, pointers to relevant papers, and other materials will be available as HTML and PDF documents on Relate and Piazza


There will be 3-5 homework assignments (TBD). The homeworks will consist of a combination of machine problems and written exercises to be submitted on the course relate website. All homeworks are due at Midnight CT on the specified day. Submitted solutions to the machine problems must be written in Python 3, using only standard libraries (numpy / scipy). We will sometimes block libraries as required. Solutions to the written problems must be submitted as LaTeX typeset PDF’s. Each question must begin on a new page. You may use WYSIWYG latex editors such as LyX if you are unfamiliar with LaTeX.


The final projects will be completed in groups of up to 4 students depending on class size. The project may consist of any combination of theoretical analysis, applications, and literature survey (possibly incorporating incorporate all three). The only constraint on the project is that it should include some aspect of probabilistic inference and/or learning. You should aim for a project suitable for submission to a machine learning conference e.g. NIPS, ICML, KDD, or domain-specific conferences e.g. CVPR, EMNLP, RECOMB.

Project Proposal

Proposals should be 1-2 pages long, and should include: * Project title and list of group members * Overview of project * Literature survey of 3 or more relevant papers * Description of data sets to use for the experiments, if applicable * Plan of activities, including final goal and how you plan to divide up the work

Final Project Report

The final project will be submitted through Relate in NIPS 2016 format i.e. main manuscript of up to 8 pages, plus one page of references. Any member of the group may submit the project.

You may include a supplement as additional pages appended to the main pdf. If you have supplements that cannot be submitted as pdf, please email a zipped file to me by the deadline. The main paper should be self-contained i.e. do not assume that the reviewer will read your supplement.

Project Presentation

Each group will give a short presentation of the course project in class. Live demonstrations of your software are highly encouraged (if applicable). Feedback from the presentation should be used to improve your project report.

Project Ideas

We will compile and distribute a list of project ideas in the first couple of weeks. Students may also develop projects based on their own research, again with the constraint that it includes some aspect of probabilistic inference and/or learning.

Course Management

Course details, homework and grades will be managed using Relate. Please self-register at here

Online Discussion Platform

We will use Piazza for our online discussion platform. You are encouraged to use Piazza to ask the course staff and your classmates and course-related questions (we will refer all email queries to Piazza). Please self-register at


Academic integrity

Feel free to discuss the assignment with each other in general terms, and to search the Web for general guidance (not for complete solutions). All solutions should be written up individually. If you make substantial use of some information from outside sources, make sure you acknowledge the sources in your solution. In particular, make sure you acknowledge all other students you worked with on the homework/projects. Failure to do this will result in a zero grade. We will follow the departmental honor code policy here:

What to do in an emergency

See this handout


Participation 10%, Homework 40%, Project 50% Project grading: 5% for the proposal, 30% for the manuscript and 15% for the presentation Participation is primarily evaluated during the project presentation portion of the course.

Late Policy

WE DO NOT ACCEPT LATE HOMEWORK. The lowest homework grade will be dropped.