BST 570-1: Probabilistic Machine Learning (Fall 2026)

Course Info

• Instructor: Seong-Hwan Jun.
• TA: TBD.
• Lectures: TBD.
• Office hours: TBD.
• 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)
• BDA3: Bayesian Data Analysis (3e) (Gelman et al.)

Assignments

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

Quizzes

• Quiz 1: TBD.
• Quiz 2: TBD.

Labs

• Lab 1: TBD.

Course Schedule

Week of Sep 7: Overview

• Slides
• Lab.

Week of Sep 14: Probability and Bayes

• Slides.
• Lab.