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 Part I  / Part II

                                                    Bias-variance analysis / Diversity

November 10, 12                      Learn++ :Incremental / NS Learning

November 17, 19                      Decision Trees / CART, ID3, C4.5

November  24                            EXAM II (see announcements)         

December 1, 3                            Unsupervised Learning / Clustering

December 8,10                          Mixture Models / EM Alg. / RVM

 

December 15,17                        Project Presentations            

                                                                                          

Bold: Midterm week

 

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