Date  Subject  Reading 
On your own  learn linear algebra  Self Test, Self Test Solutions, Strang’s Course 
W/Aug 27  Intro slides  
F/Aug 29  Probability Review slides  Bishop 1.11.4 
M/Sep 1  No class: Labor Day  
W/Sep 3  Nearest Neighbor  Bishop 2.5 
F/Sep 5  Intro to Matlab Tutorial  Coursera octave tutorial 
M/Sep 8  Decision Trees (and information theory) slides  Decision Trees by N. Nilsson, Bishop 1.6, 14.4 
W/Sep 10  Basic Point Estimation (MLE andMAP) pdf slides  Bishop 2.1, Appendix B, MLMath 
F/Sep 12  Gaussians,1D Regression slides  Bishop 1.2.4, 2.3.1–2.3.3 optional slides 
M/Sep 15  Regression  Bishop 1.1–1.4, Bishop 3.1, 3.1.1, 3.1.4, 3.1.5, 3.2, 3.3, 3.3.1, 3.3.2 
W/Sep 17  Bias Variance Decomposition Overfitting and Regularization  Bishop 1.3, 1.5, 3.2 applet 
F/Sep 19  Bias/Variance for Regression, slides  Hastie et al. 7.17.3 (supplemental reading:LASSO) 
M/Sep 22  Classification Naive Bayes slides
 
W/Sep 24  Logistic Regression slides  Naive Bayes vs Logistic Regression, Bishop 4.0, 4.2 4.5 
F/Sep 26  NB vs. LR, Basis Functions RBF slides  
M/Sep 29  Feature Selection and MDL slides  Hastie et al. 7.57.8 
W/Oct 1  more feature selection slides  consistency 
F/Oct 3  Boosting  Bishop 14.3, Schapire’s Tutorial

M/Oct 6  Neural Net slides Deep Networks  
M/Oct 6  review session 5:00 p.m. Wu & Chen  Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, CrossValidation, Boosting 
W/Oct 8  Midterm  sample exam questions and answers 
F/Oct 10  No class: Fall Break  
M/Oct 13  Kernel Methods
 Bishop 6.1,6.2 (Kernels) 
W/Oct 15  More Kernels, Lagrange Duality  lagrange examples Bishop Appendix E LagrangeMultipliers 
F/Oct 17  Support Vector Machines
 Bishop 7.1 (Max Margin) Hearst 1998 
M/Oct 20  SVM slides
 Burges 1998 
W/Oct 22  perceptron slides  supplemental reading:MIRA, Perceptrons and SVM Recap and Optimization 
F/Oct 24  Vectors, Matrices, Eigenvectors  Kosecka’s review slides 
M/Oct 27  Dim. Reduction PCA  Bishop Appendix C Properties of Matrices 
W/Oct 29  PCR and CCA  Bishop 12.1 supplemental slides 
F/Oct 31  Deep Networks  supplemental:autoencoders 
M/Nov 3  Unsupervised Learning: Clustering, Kmeans  Bishop 9.19.3 
W/Nov 5  EM
 Bishop 12.1–12.3, supplemental:Neal and Hinton 
F/Nov 7  EM and LDA slides  supplemental:LDA intro and original LDA paper 
M/Nov 10  LDA slides, generative model summary kernel summary  
W/Nov 12  Netflix  supplemental:netflix 
F/Nov 14  Project Overview and Advice and project slides; Real world machine learning and precision/recall  More Advice and python for converting text to ints 
M/Nov 17  Bayes Nets  supplemental: Koller+al, Graphical Models in a Nutshell 
W/Nov 19  Bayes Nets slides  Bishop 8.2 
F/ Nov 21  Inference in Bayes Nets ML speed slides  
M/Nov 24  Hidden Markov Models and slides  Rabiner’s HMM Tutorial supplemental:Bishop 13.1–2 
W/Nov 26  No class — Happy Thanksgiving!!  
F/Nov 28  No class — Happy Thanksgiving!!  
M/Dec 1  spectral Hidden Markov Models  
W/Dec 3  Causality and Active learning  
F/Dec 5  Big Data  paper 
M/Dec 8  Final project awards; Final Review Other material
 LIONbook  a quick review; After CIS520 
M/Dec 15  Review Session: 5:00–6:00 pm Wu & Chen  
W/Dec 17  Final: 9:00am11:00am COHN G17  last year’s final and solution 