Schedule

Date Week Topic Reading PDFs
Jan 12 1 Intro / Welcome HTF 1 Lecture 1
Jan 14   Bayes Decision Theory DHS 2 Lecture 2
Jan 19 2 Gaussian Methods HTF 4 Lecture 3
Jan 21   Subspace Methods Leonardis Lecture 4
Jan 26 3 Discussion 1 - Turk [19] and Kumar [1]    
Jan 28   Discussion 2 - Guyon [15] and Xiang [6]    
Feb 2 4 Ensemble Methods HTF 10 Lecture 5
Feb 4   Discussion 3 - Viola [11] and Breiman [18]    
Feb 9 5 HW1 Tour    
Feb 11   Discussion 4 - Mozos [10] and Csurka [12]    
Feb 16 6 Hidden Markov Models DHS 3 Lecture 6
Feb 18   Discussion 5 - Leibe [13] and Antani [16]    
Feb 23 7 Discussion 6 - Choi [5] and Lotte [7]    
Feb 25   Discussion 7 - Rabiner [20] and Yang [4]    
Mar 1 8 HW2 Tour    
Mar 3   Prototype based Methods HTF 13 Lecture 7
Mar 8 9 Discussion 8 - Savarese [8] and Hoffman [2]    
Mar 10   Kernel Methods and other tricks HTF 6 Lecture 8
Mar 15 10 Maximum Margin Classfiers HTF 12 Lecture 9
Mar 17   Discussion 9 - Campbell [9] and Schüldt [14]    
Mar 22 11 Spring Break    
Mar 24 Spring Break    
Mar 29 12 Deep Learning - Setup Workshop    
Mar 31 Discussion 10 - Burges [17] and Szegedy [3]    
Apr 5 13 Neural Networks and Deep Learning Lecture 10
Apr 7 Deep Learning - Part Two Lecture 11
Apr 12 14 Tour of HW3    
Apr 14 Unsupervised learning HTF 14 Lecture 12
Apr 19 15 Discussion 13    
Apr 21 Sequential Learning   Lecture 13
Apr 26 16 Exam week    
Apr 28 Exam week    

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