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

• PML1: Probabilistic Machine Learning: An Introduction (Murphy)
• PML2: Probabilistic Machine Learning: Advanced Topics (Murphy)
• PRML: Pattern Recognition and Machine Learning (Bishop)
• BDA3: Bayesian Data Analysis (3e) (Gelman et al.)

Assignments

• Assignment 1.
• Assignment 2.
• Assignment 3.
• Assignment 4.
• Assignment 5.
• Assignment 6.

Quizzes

• Quiz 1: Oct 21st.
• Quiz 2.

Course Schedule

Week 1 (Sep 7): Overview

• Slides
• Lab

Week 2 (Sep 14): Probability and Bayes

• Slides
• Lab

Week 3 Sep 21: GMM and EM

• Slides
• Lab

Week 4 Sep 28: HMM

• Part 1 slides