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start [2016/08/29 18:03] – hjstart [2019/08/22 21:28] (current) – hj
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 ~~NOTOC~~ ~~NOTOC~~
-====== EECS 6327 Probabilistic Models & Machine Learning (Fall 2016) ======+====== EECS 6327 Probabilistic Models & Machine Learning (Winter 2018) ======
  
 ===== Description  ===== ===== Description  =====
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 The course introduces some probabilistic models and machine learning methods. The covered topics may include:    The course introduces some probabilistic models and machine learning methods. The covered topics may include:   
   * Bayesian Decision theory, Generative vs Discriminative modelling   * Bayesian Decision theory, Generative vs Discriminative modelling
-  * Generative Models (1) - multivariate Gaussian, Gaussian mixture model (GMM), hidden Markov model (HMM), Multinomial, Markov chain model, n-gram, finite mixture models+  * 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)   * 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, logistic regression, support vector machines (SVM), sparse kernel machines +  * 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, recurrent neural networks, convolutional neural networks +  * Discriminative Models (2) - neural networks (NN),  back-propagation,  deep learning,   auto-encoder; recurrent neural networks, convolutional neural networks 
-  * Statistical Modeling Methods - maximum likelihood estimation (MLE), Expectation-Maximization (EM), discriminative training (DT)+  * 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 ===== ===== Announcements =====
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 ===== Lecture Times ===== ===== Lecture Times =====
  
-  * Section A: Wednesdays and Fridays, 1:30pm - 3:00pm, FRQ 320 (TBA)+  * Section A: Tuesdays and Thursdays, 4:00pm - 5:30pm, located at <del>BC228</del> **SC 214**.
  
 ===== Lecturer ===== ===== Lecturer =====
  
   * Prof. [[http://www.cse.yorku.ca/~hj|Hui Jiang]] @ CSEB3014   * Prof. [[http://www.cse.yorku.ca/~hj|Hui Jiang]] @ CSEB3014
start.1472493833.txt.gz · Last modified: 2016/08/29 18:03 by hj
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