BST 570-1: Probabilistic Machine Learning (Fall 2026)
Course Info
- Instructor: Seong-Hwan Jun.
- Lectures: MW 1:30–2:40 PM; Th 1:30–2:20 PM. Saunders Research Building 1.404.
- Syllabus
Assessment
- 40% assignments (5-6 over the term).
- One problem broken into subproblems.
- Each student presents part of their solution in class.
- 10% x 2 quizzes, multiple choice questions.
- 10% labs, graded for completion and participation.
- 30% paper presentation.
- Read a research paper closely and present it to the class.
- Critically read and assess machine learning research.
Resources
Assignments
- Assignment 1
- Assignment 2
- The data files for the assignment, place it under
data/:
- Assignment 3
- Assignment 4
- Assignment 5
- Assignment 6
Quizzes
Course Schedule
Week 1 (Sep 7): Overview
Week 2 (Sep 14): Probability and Bayes
Week 3 (Sep 21): GMM and EM
Week 4 (Sep 28): HMM
Week 5 (Oct 5): HMM
Week 6 (Oct 12): Belief propagation