Machine Learning Course

Machine Learning Course

given by Jie Tang， 2011

Course Description

This course provides an advanced introduction to machine learning. Topics include: 
  1. Support Vector Machine; 
  2. Topic model: Probabilistic Latent Semantic Indexing, Latent Dirichlet Allocation, Author Topic Model, Correlated Topic Model;
  3. Restricted Boltzmann Machines: basic model, Discriminative RBM, Factored Conditional RBM, Deep belief networks, Metropolis-Hasting;
  4. Markov random fields: Hidden Markov model, Maximum entropy Markov models, Conditional random fields, Loopy belief propagation;
  5. Factor graph: Affinity propagation, Semi-supervised factor graph, Connection with other graphical models;
  6. Non-parametric learning: Chinese restaurant process, Mont-Carlo Markov Chain based algorithm, Dirichlet process mixture models, Hierarchical Dirichlet process, Indian Buffet process.

Course Materials

There is no required text for this course. Notes will be posted periodically on the course web site.
The following books are recommended as optional reading:
  • Daphne Koller and Nir Friedman. Probabilistic Graphical Models. MIT Press, 2009.
  • Christopher M. Bishop. Pattern Recognition and Machine Learning, Springer, 2007.
  • John Hopcroft. Computer Science Theory for the Information Age. 2011.
  • Michael I. Jordan. An Introduction to Probabilistic Graphical Models. University of California, Berkeley. June 30, 2003.
  • Martin J. Wainwright and Michael I. Jordan. Graphical Models, Exponential Families, and Variational Inference, Foundations and Trends in Machine Learning, V1 (1-2), 2008.
  • Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. Springer, 2003.
  • Yoshua Bengio. Learning Deep Architectures for AI. Foundations and Trends in Machine Learning, V2 (1), 2009.
  • David J.C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.

Teaching Staff

Jie Tang

Associate Professor. 

Research interests include semantic web, social network mining and machine learning.

HP: http://keg.cs.tsinghua.edu.cn/persons/tj 

 

Head TA: Yang Yang
Office: FIT 1-308
E-mail: sherlockbourne@gmail.com 
Phone: 62788788-20, 15001156350
 
TAs:
Wenbin Tang, tangwb06@gmail.com
Jianfei Wang, me@thinxer.com
Daifeng Li, ldf3824@yahoo.com.cn

Reading List

We recommend papers in below lists from different conferences.

ICDM 2011:

The International Conference on Machine Learning (ICML) is the premier conference for machine learning research. It is organized by the International Machine Learning Society (IMLS).

http://arnetminer.org/lab-datasets/ml/reading-list-icm11.pdf

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