Probabilistic Graphical Models
Implementations written during the PGM class at UdeM (ift6269):
- some classifiers: Fisher LDA, logistic regression with IRLS, QDA
- some clustering algorithms: k-means, Gaussian mixtures
- a Hidden Markov Model with inference and learning algorithms (Baum-Welch, Viterbi decoding etc..)
- approximate inference in a simple Ising model (Gibbs sampling and variational inference)