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 (4 or 5).
  • 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)

Assignments

• Assignment 1
• Assignment 2
  • The data files for the assignment, place it under data/:
    • steps.csv
    • steps_subjects.csv
• Assignment 3
  • Data
• Assignment 4
• Assignment 5

Quizzes

• Quiz 1: Oct 15th.
• 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
• Part 2 slides

Week 5 (Oct 5): HMM

• HMM Part 2 slides (updated)
• Discuss Assignment 1.

Week 6 (Oct 12): Belief propagation

• Lab
• Belief propagation on trees

Week 7 (Oct 19): Wrap exact inference/Discuss Quiz 1.

Week 8 (Oct 26): Undirected graphical models

• Undirected graphical models
• Discuss Assignment 2.
• Lab

Week 9 (Nov 2): Markov chain Monte Carlo (why it works)

• MCMC
• HMC
• Lab

Week 10 (Nov 9): Dirichlet Process Mixture Model and SMC

• Markov Chain Sampling Methods for Dirichlet Process Mixture Models
  • Paper
  • Slides