Date  Subject  Reading 
On your own  learn linear algebra  Self Test, Self Test Solutions, Strang’s Course 
W/Aug 31  Intro, intro_slides  
F/Sep 2  Probability Review  Bishop 1.11.4, slides 
M/Sep 5  No class: Labor Day  
W/Sep 7  Nearest Neighbor norm slides  Bishop 2.5 
F/Sep 9  Intro to Matlab Tutorial  Coursera octave tutorial 
M/Sep 12  Decision Trees (and information theory) slides  Decision Trees by N. Nilsson, Bishop 1.6 Bishop 14.4 
W/Sep 14  Basic Point Estimation (MLE andMAP) PDF slides  Bishop 2.1, Appendix B, MLMath covariance 
F/Sep 16  Gaussians,1D Regression slides  Bishop 1.2.4, Bishop 2.3.12.3.3 optional slides 
M/Sep 19  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 21  Overfitting and Regularization, Bias Variance Decomposition, Bias/Variance for Regression  Bishop 1.3, 1.5, 3.2 applet 
F/Sep 23  regression penalty slides Stepwise, streamwise, stagewise  Hastie et al. 7.17.3 (supplemental reading:LASSO) 
M/Sep 26  Classification Naive Bayes slides
 
W/Sep 28  Logistic Regression  Naive Bayes vs Logistic Regression, Bishop 4.0, 4.2 4.5 
F/Sep 30  NB vs. LR, Basis Functions, RBFs robust regression  short videos on hat matrix and stepwise regression and RBFs 
M/Oct 3  MDL slides, MDL feature selection review  Hastie et al. 7.57.8 
W/Oct 5  Boosting  Bishop 14.3 , Schapire’s Tutorial

F/Oct 7  No class: Fall Break  
M/Oct 10  Neural Net slides Supervised Deep Networks  supplemental: deep net tutorial 
W/Oct 12  more deep learning  
R/Oct 13  review session 5:00 p.m. meyerson Room: B(asement)  Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, CrossValidation, Boosting Complexity consistency 
F/Oct 14  Midterm  sample exam questions and answers more examples are part of the old final exam below 
M/Oct 17  Kernel Methods kernel examples kernel regression
 Bishop 6.1,6.2 (Kernels) Supplemental: more on kernels 
W/Oct 19  More Kernels, Lagrange Duality Lagrange examples  Bishop Appendix E LagrangeMultipliers 
F/Oct 21  Support Vector Machines
 Bishop 7.1 (Max Margin) Hearst 1998 
M/Oct 24  SVM slides
 Burges 1998 
W/Oct 26  Perceptron slides  supplemental reading:MIRA, Perceptrons and SVM Recap and Optimization 
F/Oct 28  Vectors, Matrices, Eigenvectors  Kosecka’s review slides 
M/Oct 31  Dim. Reduction PCA eigenwords  Bishop Appendix C Properties of Matrices 
W/Nov 2  Unsupervised Deep Networks  
F/Nov 4  PCR, PLS and CCA slides  Bishop 12.1 supplemental slides 
M/Nov 7  Unsupervised Learning: Clustering, Kmeans EM  Bishop 9.19.3 
W/Nov 9  EM Generative PCA  Bishop 12.1–12.3, supplemental:Neal and Hinton 
F/Nov 11  LDA slides  supplemental:LDA intro and original LDA paper 
M/Nov 14  Generative model summary, loss functions and ML speed slides  
W/Nov 16  Netflix  supplemental:netflix 
F/Nov 18  Project overview and advice and project slides; Real world machine learning  More advice and python for converting text to ints 
M/Nov 21  Bayes Nets  supplemental: Koller+al, Graphical Models in a Nutshell 
W/Nov 23  no class  
F/Nov 25  No class — Happy Thanksgiving!!  
M/Nov 28  Bayes Nets slides Questions  Bishop 8.2 
W/ Nov 30  Inference in Bayes Nets Hidden Markov Models and HMM slides  Rabiner’s HMM Tutorial, supplemental:Bishop 13.1–2 
F/Dec 2  Recurrent Neural Networks  
M/Dec 5  Active learning and Causality  
W/Dec 7  BIg Data slides  Unreasonable effectiveness of data, supplemental:sampling 
F/Dec 9  The Future of ML and humanity  
M/Dec 12  Final project awards; Final Review Other material Review Questions
 LIONbook  a quick review; After CIS520 
W/Dec 21  Review Session: 4:00–5:00 pm WTBD  
Th/Dec 22  Final: 9:00am11:00am Towne 100  2014 final and solution 