Announcements
- Prerequisites: CS 5800 or CS 7800 with a minimum grade of C-. I will not enforce these pre-requisites this year. However, note that you are taking the course at your own risk. I will assume the knowledge of algorithms as well as intellectual capacity and work ethic of a student who passed such a course. I cannot add you to the course directly. Please show this note to your graduate advisor to enroll you in the course if the system blocks you from doing it due to prerequisites.
- How to prepare for the course? See here for some guidance.
- First class: Friday, September 11, see academic calendar
- Thanksgiving break: November 26-27, no class on November 27
- Midterm exam: Week 7, Friday or Week 8, Tuesday, in class.
- Final exam: Friday, December 4, in class
- Mini project report due: Monday, December 7
- This class will be held on ground.
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Last updated: September 22, 2026
Weekly Schedule
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Week 3, September 21 ☀️
Topics
• Bayesian estimation
• Expectation-maximization algorithm
Reading materials
- Textbook #1 (Bishop): Mixture Models and EM (Chapter 9)
- Sections 9.1, 9.2, 9.3, 9.4 (light reading)
Handouts and code
• EM algorithm code
Homework assignments
• Assignment #1 available here ☀️
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Week 2, September 14
Topics
• Random variables
• Principles of parameter estimation
Reading materials
• Textbook #1 (Bishop): Introduction (Chapter 1)
- Lecture notes (Radivojac & White): parameter estimation
Handouts and code
• Random variables slides
• Parameter estimation slides
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Week 1, September 7
Topics
- Class overview and logistics
- Short review of probability theory
Reading materials
- Textbook #1 (Bishop): Introduction (Chapter 1)
- Lecture notes (Radivojac & White): probability
Handouts and code
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