Date  Subject  Reading 
On your own  learn linear algebra  Self Test, Self Test Solutions, Strang’s Course 
W/Aug 28  Intro slides  
F/Aug 30  Probability Review slides  Bishop 1.11.4 
M/Sep 2  No class: Labor Day  
W/Sep 4  Nearest Neighbor  Bishop 2.5 
F/Sep 6  Intro to Matlab Tutorial  Coursera octave tutorial 
M/Sep 9  Decision Trees (and information theory) slides  Decision Trees by N. Nilsson, Bishop 1.6, 14.4 
W/Sep 11  Basic Point Estimation (MLE andMAP) pdf slides  Bishop 2.1, Appendix B, MLMath 
F/Sep 13  Gaussians,1D Regression slides  Bishop 1.2.4, 2.3.1–2.3.3 optional slides 
M/Sep 16  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 18  Bias Variance Decomposition  Bishop 1.3, 1.5, 3.2 applet 
F/Sep 20  Bias/Variance for Regression, Overfitting and Regularization slides  Hastie et al. 7.17.3 (supplemental reading:LASSO) 
M/Sep 23  Classification Naive Bayes slides
 
W/Sep 25  Logistic Regression slides  Naive Bayes vs Logistic Regression, Bishop 4.0, 4.2 4.5 
F/Sep 27  NB vs. LR, Basis Functions  
M/Sep 30  Feature Selection and MDL slides  Hastie et al. 7.57.8 
W/Oct 2  more feature selection slides  
F/Oct 4  Boosting  Bishop 14.3, Schapire’s Tutorial

M/Oct 7  Neural Net slides  
M/Oct 7  review session 7:00 p.m. DRLB A8  Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, CrossValidation, Boosting 
W/Oct 9  Midterm  
F/Oct 11  No class: Fall Break  Please take survey 
M/Oct 14  Kernel Methods
 Bishop 6.1,6.2 (Kernels) 
W/Oct 16  More Kernels, Lagrange Duality  Bishop Appendix E LagrangeMultipliers 
F/Oct 18  Support Vector Machines
 Bishop 7.1 (Max Margin) Hearst 1998 
M/Oct 21  SVM slides
 Burges 1998 
W/Oct 23  perceptron slides  supplemental reading:MIRA, Perceptrons and SVM Recap and Optimization 
F/Oct 25  Vectors, Matrices and Tensors; Eigenvectors  Kosecka’s review slides 
M/Oct 28  eigenvectors, SVD  Bishop Appendix C Properties of Matrices 
W/Oct 30  Dim. Reduction PCA  Bishop 12.1 
F/Nov 1  PCR and CCA  supplemental slides 
M/Nov 4  Deep Networks  supplemental:autoencoders 
W/Nov 6  Unsupervised Learning: Clustering, Kmeans  Bishop 9.19.3 
F/Nov 8  EM
 Bishop 12.1–12.3, supplemental:Neal and Hinton 
M/Nov 11  LDA slides  supplemental:LDA intro and original LDA paper 
W/Nov 13  Netflix  supplemental:netflix 
F/Nov 15  Project Overview and Advice and slides; Real world machine learning  More Advice 
M/Nov 18  Bayes Nets  supplemental: Koller+al, Graphical Models in a Nutshell 
W/Nov 20  Bayes Nets  Bishop 8.2 
F/ Nov 22  Bayes Nets  
M/Nov 25  Inference in Bayes Nets Hidden Markov Models  Rabiner’s HMM Tutorial supplemental:Bishop 13.1–2 
W/Nov 27  No class — Happy Thanksgiving!!  
F/Nov 29  No class — Happy Thanksgiving!!  
M/Dec 2  spectral Hidden Markov Models  
W/Dec 4  Causality and Active learning  
F/Dec 6  Big Data  paper 
M/Dec 9  Final project awards; Final Review Other material
 LIONbook  a quick review; After CIS520 
M/Dec 16  Review Session: 4:00–5:00  Skirkanich Hall Berger Auditorium 
W/Dec 18  Final! 9:00am11:00am Dhirubhai Ambani Auditorium (JMHH G06)  this year’s final and solution 