Probabilistic Machine Learning (Spring 2025)

Instructor: Seong-Hwan Jun.

Outline

Course Schedule

Jan 21: Overview; condensed PDF slides; Python primer.

Jan 23: Probability and Bayes; condensed PDF slides; sampling.ipynb.

Jan 28 Part 1: Optimization and Sampling; condensed PDF slides; optimization.ipynb.

Jan 28 Part 2: Directed Graphical Models; condensed PDF slides.

Jan 30: Hidden Markov models and forward pass (PML2 9.1-9.2); forward.ipynb.

Feb 4: HMM backward pass and parameter estimation (PML2 9.1-9.2); Kalman filter (PML2 8.1-8.2). forward_backward.ipynb.

Feb 6: Intro to phylogenetics; Felsenstein.ipynb.

Feb 11: EM algorithm and Gaussian mixture model. GMM.ipynb.

Feb 13: Factor graph and sum-product algorithm.

Feb 18: Sequential Importance Sampling and Bootstrap Particle Filter. smc.ipynb.

Feb 20: Discussion of On-line inference for hidden Markov models via particle filters. Slides.

Feb 25: Markov chain Monte Carlo methods. Slides.

Feb 27: Discussion of Markov Chain Sampling Methods for Dirichlet Process Mixture Models. Slides.

Mar 4: Discussion of HW1. HW2 discussion: Problem 1 and Problem 2.

Mar 6: Hamiltonian Monte Carlo. Slides.

Mar 11-13: Spring break (no classes).