Lectures

No. Topic Handouts
1. Introduction .zip
2. Probability Review .zip
3. Bayesian Decision Theory 1 .zip
4. Bayesian Decision Theory 2 .zip
5. Bayesian Decision Theory Case Studies .zip
6. Maximum Likelihood Estimation .zip
7. Kernel Density Estimation .zip
8. K-Nearest Neighbor Density Estimation .zip
9. Linear Discriminant Functions .zip
10. Kernel Methods .zip
11. Support Vector Machines .zip
12. Ensemble Methods .zip
13. Clustering methods -
14. Feature Selection and Performance Estimation -
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Individual Study

Textbook and Readings

  1. Richard O. Duda, Peter E. Hart , David G . Stork, "Pattern Clasification", John Wiley and Sons, 2001.
  2. C.M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
  3. S. Theodoridis, K. Koutroumbas, “Pattern Recognition”, 2-nd Edition, Academic Press, 2008.
  4. K. Murphy, “Machine Learning: A Probabilistic Perspective”, The MIT Press, 2012.