~~NOTOC~~
====== EECS 6327 Probabilistic Models & Machine Learning (Winter 2018) ======

===== Description  =====

The course introduces some probabilistic models and machine learning methods. The covered topics may include:   
  * Bayesian Decision theory, Generative vs Discriminative modelling
  * Generative Models (1) - multivariate Gaussian, Gaussian mixture model (GMM),  Multinomial, Markov chain model, n-gram
  * Generative Models (2) - graphical models, directed vs. indirected graphical models, exact inference, approximate inference (loopy belief propagation, variational inference, Monte Carlo methods)
  * Discriminative Models (1) - linear discriminant, linear regression, lasso, logistic regression, support vector machines (SVM), sparse kernel machines
  * Discriminative Models (2) - neural networks (NN),  back-propagation,  deep learning,   auto-encoder; recurrent neural networks, convolutional neural networks
  * Advanced models: hidden Markov model (HMM),  Latent Dirichlet Allocation (LDA), Conditional Random Fields (CRF), Convolutional Neural Nets, Recurrent Neural Nets and LSTMs
  * Advanced topics: Learnability, Gaussian Processes, Ensemble Methods, Reinforcement Learning, etc.


===== Announcements =====

  * Check the left tab [[:whats_new|What's New]]

===== Lecture Times =====

  * Section A: Tuesdays and Thursdays, 4:00pm - 5:30pm, located at <del>BC228</del> **SC 214**.

===== Lecturer =====

  * Prof. [[http://www.cse.yorku.ca/~hj|Hui Jiang]] @ CSEB3014