EECS6327

User Tools

Site Tools

You are here: EECS 6327 Probabilistic Models & Machine Learning (Winter 2018)
Trace: • EECS 6327 Probabilistic Models & Machine Learning (Winter 2018) • Advanced Topics for self-study and Presentation • What's New? • projects
start

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision		Previous revision
start [2016/08/29 19:04] – hjstart [2019/08/22 21:28] (current) – hj
Line 1: Line 1:
 ~~NOTOC~~ ~~NOTOC~~
-====== EECS 6327 Probabilistic Models & Machine Learning (Fall 2016) ======+====== EECS 6327 Probabilistic Models & Machine Learning (Winter 2018) ======
  
 ===== Description  ===== ===== Description  =====
Line 9: Line 9:
   * 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, lasso, 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
   * Advanced models: hidden Markov model (HMM),  Latent Dirichlet Allocation (LDA), Conditional Random Fields (CRF), Convolutional Neural Nets, Recurrent Neural Nets and LSTMs   * 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.   * Advanced topics: Learnability, Gaussian Processes, Ensemble Methods, Reinforcement Learning, etc.
Line 20: Line 20:
 ===== Lecture Times ===== ===== Lecture Times =====
  
-  * Section A: Wednesdays and Fridays, 1:30pm - 3:00pm, location (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.1472497456.txt.gz · Last modified: 2016/08/29 19:04 by hj
Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki