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CIS 520 - Fall 08
Schedule

 

Date Subject Problem Set Reading Notes
Sep 3 Intro; Point Estimation   Bishop 2.1, Appendix B  
Sep 8 Matlab Tutorial by Ben Sapp      
Sep 10 Gaussians, Linear models, Regression PS 1 out on Friday in BlackBoard Bishop 1.1 to 1.4,
Bishop 3.1, 3.1.1, 3.1.4, 3.1.5, 3.2, 3.3, 3.3.1, 3.3.2
Least Squares Regression Applet
Sep 15 Bias-Variance Decomposition, Naive Bayes and Logistic Regression   Bishop 1.3, 1.5, 3.2; Mitchell's Chapter on Naive Bayes and Logistic Classification Applet
Sep 17 Generative vs Discriminative, Naive Bayes and Logistic Regression   Bishop 4.0, 4.2, 4.3, 4.4, 4.5; Mitchell's Chapter on Naive Bayes and Logistic  
Sep 22 Logistic Regression Continued, Decision Trees   Bishop 4.0, 4.2, 4.3, 4.4, 4.5; Bishop 1.6 (Information Theory); Bishop 14.4 (Tree Models) Decision trees Applet
Sep 24 Decision Trees   Bishop 1.6 (Information Theory); Bishop 14.4 (Tree Models), Nilsson's Chapter  
Sep 29 Decision Trees and Boosting PS 1 Due at 5pm Bishop 14.3 (Boosting); Schapire's Boosting Tutorial  
Oct 1 Boosting, Regularization, Cross-validation, Model Selection PS2 out on Thursday Bishop 1.3, 3.1.4;  
Oct 6 Cross-validation, Model Selection; Neural Networks   Bishop 3.1.4; Bishop 5.1 Optional Reading: Ron Kohavi's paper, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.
Oct 8 Neural Networks; Nonparametric Methods   Bishop 5.1-5.3 (NNs); Bishop 2.5 Nonparametric Methods  
Oct 13 Happy Fall Break      
Oct 15 Nonparametric Methods, Nearest Neighbors   Bishop 2.5 Nonparametric Methods  
Oct 20 Midterm     Previous Midterms from CMU ML class
  • The 2003 midterm exam
  • Handwritten annotations to 2003 midterm by Ajit on Thursday evening
  • Solutions to the 2003 midterm exam
  • The 2004 midterm exam
  • Solutions to the 2004 midterm exam
  • Solutions to the 2005 midterm exam
  • The Spring 2006 midterm exam
  • Partial Solutions to the Spring 2006 midterm exam
  • The Fall 2006 midterm exam
  • Solutions to the Fall 2006 midterm exam
  • The Spring 2007 midterm exam
  • Solutions to the Spring 2007 midterm exam
  • Oct 22 Kernel Methods, Support Vector Machines   (Bishop 6.1,6.2) Kernels
    (Bishop 7.1) Maximum Margin Classifiers Additional Material:
    Hearst 1998: High Level Presentation
    Burges 1998: Detailed Tutorial
     
    Oct 27 Kernel Methods, Support Vector Machines cont.   (Bishop 6.1,6.2) Kernels
    (Bishop 7.1) Maximum Margin Classifiers
     
    Oct 29 Kernel cont., Generalization Bounds   (Bishop 6.1,6.2) Kernels
    (Bishop 7.1) Maximum Margin Classifiers
    LIBSVM Applet
    Nov 3 Generalization Bounds, PAC Learning   Goldman's COLT survey, sections 1-3.1
    Avrim Blum's course handout on tail inequalities
     
    Nov 5 Generalization Bounds, PAC Learning PS 3, Project Out    
    Nov 10 Project Review      
    Nov 12 Bayes nets - Representation     (Bishop 8.1,8.2) Bayesian Networks  
    Nov 17 Bayes nets - Inference     (Bishop 8.4.1,8.4.2) - Inference in Chain/Tree Structures
    Rabiner's HMM Tutorial
     
    Nov 19 Bayes nets - Inference, cont.      
    Nov 24 Unsupervised Learning, Clustering PS 4 out (Bishop 9.1, 9.2) - K-means, Mixtures of Gaussians  
    Nov 26 No class -- Happy Thanksgiving!      
    Dec 1 K-Means, Clustering   (Bishop 9.1, 9.2) - K-means, Mixtures of Gaussians  
    Dec 3 EM   (Bishop 9.3, 9.4) - EM
    Neal and Hinton EM paper
    EM: Mixture of Gaussians Applet  
    Dec 12 Final: 12-3pm, Wu & Chen     Practice Exams: from CMU ML class