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--- start [2007/08/14 15:36] – external edit 127.0.0.1
+++ start [2016/09/12 20:57] – hj
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 ~~NOTOC~~
-====== Your Course  ======
+====== EECS 6327 Probabilistic Models & Machine Learning (Fall 2016) ======
 ===== Description  =====
-The course lays the conceptual foundation of object-oriented programming. It covers delegation and contracts, encapsulation and API programming, aggregation and the collections framework, inheritance and polymorphism, all from the client's perspective.
+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: Mondays and Fridays, 11:00am - 12:00pm, CSE 111
+  * Section A: Wednesdays and Fridays, 1:00pm - 2:30pm, located at <del>RS536</del> **BSB
+**.
+===== Lecturer =====
+  * Prof. [[http://www.cse.yorku.ca/~hj|Hui Jiang]] @ CSEB3014