Lecture Notes

Syllabus and instructional objectives  are also available for download. Make sure that you check the last updated notice at the bottom, since you will be responsible from the latest version at all times.

 

Date                                         Topic                                   

Pre-class                                     Lecture 0 / Math Fundamentals

                                         (Also see announcements page)

September 1, 3*                        Introduction / Bayes Theory

September 8, 10                        Bayes Decision Theory

September 15, 17                      Density Estimation

September 22, 24                     PCA/FLD  /

Sept/Oct    29, 1                        Linear Classifiers

October 6, 8                              Multilayer Perceptrons - EXAM I

October 13, 15                           RBF networks / Exam review

October 20, 22                           Support Vector Machines (SVM)

October 27, 29                           SVMs (cont.)/ Structural R.M.

November 3, 5                           Ensemble Learning / Boosting

November 10, 12                      Incremental / Nonstationary Learning

November 17, 19                      Mixture Models / EM Alg. / RVM

November  24*, 26*                 Unsupervised Learning / Clustering

December 1, 3                            Decision Trees / CART, ID3, C4.5

December 8,10                          Hidden Markov Models

December 15,17                        Project Presentations            

                                                                                          

Bold: Midterm week

 

(*Dr. Polikar may be away—Class will be delivered via Skype or
rescheduled)

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