Schedule

Date Week Topic Reading PDFs
Apr 4 1 Intro / Welcome HTF 1 Lecture 1
Apr 6   Bayes Decision Theory DHS 2 Lecture 2
Apr 11 2 Gaussian Methods HTF 4 Lecture 3
Apr 13   Subspace Methods Leonardis Lecture 4
Apr 18 3 Discussion 1 - Turk [22], Hoffman [2], Fei-Fei [12] and Kumar [1]    
Apr 20   Discussion 2 - Guyon [17], Yang [4], Cheng [21] and Xiang [7]    
Apr 25 4 Ensemble Methods HTF 10 Lecture 5
Apr 27   HW1 Tour    
May 2 5 Discussion 3 - Viola [14], Schueldt [16], Lotte [8] and Breiman [20]    
May 4   Discussion 4 - Savarese [9], Quinlen [24], Mozos [11] and Csurka [13]    
May 9 6 Hidden Markov Models DHS 3 Lecture 6
May 11   Prototype based Methods HTF 13 Lecture 7
May 16 7 Kernel Methods and other tricks HTF 6 Lecture 8
May 18   HW2 Tour / Maximum Margin Classifiers HTF 12 &Lecture 9;
May 23 8 Neural Networks HTF 12 Lecture 10
May 25   Deep Learning Lecture 11
May 30 9 Discussion 5 - Malisiewicz [6], Szegedy [3], Leibe [15] and Antani [18]    
Jun 1   Discussion 6 - Freund [19], Campbell [10], Choi [5] and Rabiner [23]    
Jun 6 10 Deep Learning - Part Two Lecture 12
Jun 8 Unsupervised learning HTF 14 Lecture 12
Jun 13 Exam week