Date  Subject  Reading 
On your own  learn linear algebra  Self Test, Self Test Solutions, Strang’s Course 
W/Aug 26  Intro slides  
F/Aug 28  Probability Review  Bishop 1.11.4, slides 
M/Aug 31  Nearest Neighbor  Bishop 2.5 
W/Sep 2  Decision Trees (and information theory) slides  Decision Trees by N. Nilsson, Bishop 1.6 Bishop 14.4 
F/Sep 4  Intro to Matlab Tutorial  Coursera octave tutorial 
M/Sep 7  No class: Labor Day  
W/Sep 9  Basic Point Estimation (MLE andMAP) pdf slides  Bishop 2.1, Appendix B, MLMath covariance 
F/Sep 11  Gaussians,1D Regression slides  Bishop 1.2.4, Bishop 2.3.12.3.3 optional slides 
M/Sep 14  Regression more_regression  Bishop 1.11.4, Bishop 3.1, 3.1.1, 3.1.4, 3.1.5, 3.2, 3.3, 3.3.1, 3.3.2 
W/Sep 16  Bias Variance Decomposition Overfitting and Regularization  Bishop 1.3, 1.5, 3.2 applet 
F/Sep 18  Bias/Variance for Regression, regression penalty slides Stepwise, streamwise, stagewise  Hastie et al. 7.17.3 (supplemental reading:LASSO) 
M/Sep 21  Classification Naive Bayes slides
 
W/Sep 23  Logistic Regression  Naive Bayes vs Logistic Regression, Bishop 4.0, 4.2 4.5 
F/Sep 25  No class; Pope in town :( NB vs. LR, Basis Functions RBF slides and video, short videos on hat matrix and stepwise regression  
M/Sep 28  MDL slides  Hastie et al. 7.57.8 
W/Sep 30  Boosting  Bishop 14.3 , Schapire’s Tutorial

F/Oct 2  feature selection review  consistency 
M/Oct 5  Neural Net slides Supervised Deep Networks  supplemental: deep net tutorial 
T/Oct 6  review session 5:00 p.m. Heilmeier Hall  Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, CrossValidation, Boosting Complexity 
W/Oct 7  Midterm  sample exam questions and answers 
F/Oct 9  No class: Fall Break  
M/Oct 12  Kernel Methods kernel examples
 Bishop 6.1,6.2 (Kernels) Supplemental: more on kernels 
W/Oct 14  More Kernels, Lagrange Duality Lagrange examples  Bishop Appendix E LagrangeMultipliers 
F/Oct 16  Support Vector Machines
 Bishop 7.1 (Max Margin) Hearst 1998 
M/Oct 19  SVM slides
 Burges 1998 
W/Oct 21  perceptron slides  supplemental reading:MIRA, Perceptrons and SVM Recap and Optimization 
F/Oct 23  Vectors, Matrices, Eigenvectors  Kosecka’s review slides 
M/Oct 26  Dim. Reduction PCA eigenwords  Bishop Appendix C Properties of Matrices 
W/Oct 28  Unsupervised Deep Networks  
F/Oct 30  PCR, PLS and CCA slides  Bishop 12.1 supplemental slides 
M/Nov 3  Unsupervised Learning: Clustering, Kmeans EM  Bishop 9.19.3 
W/Nov 4  EM generative PCA  Bishop 12.1–12.3, supplemental:Neal and Hinton 
F/Nov 6  LDA slides  supplemental:LDA intro and original LDA paper 
M/Nov 9  generative model summary, loss functions and ML speed slides  
W/Nov 11  Netflix  supplemental:netflix 
F/Nov 13  Project Overview and Advice and project slides; Real world machine learning  More Advice and python for converting text to ints 
M/Nov 16  Bayes Nets  supplemental: Koller+al, Graphical Models in a Nutshell 
W/Nov 18  Bayes Nets slides  Bishop 8.2 
F/ Nov 20  Inference in Bayes Nets Hidden Markov Models and HMM slides  Rabiner’s HMM Tutorial supplemental:Bishop 13.1–2 
M/Nov 23  Recurrent Neural Networks  
W/Nov 25  no class  
F/Nov 27  No class — Happy Thanksgiving!!  
M/Nov 30  Active learning and Causality  
W/Dec 2  Big Data  paper 
F/Dec 4  The Future of ML and humanity  
M/Dec 7  Final project awards; Final Review Other material
 LIONbook  a quick review; After CIS520 
F/Dec 11  Review Session: 5:00–6:00 pm Wu & Chen ??  
M/Dec 14  Final: 9:00am11:00am COHN G17  last year’s final and solution 