Date  Subject  Reading 
On your own  learn linear algebra, basic probability  Self Test, Self Test Solutions, Strang’s Course Probability Review Bishop 1.11.4, slides 
W/Aug 30  Intro, intro_slides  
F/Sep 1  Intro to Matlab Tutorial  Coursera octave tutorial 
M/Sep 4  No class: Labor Day  
W/Sep 6  Local learning Norm slides  Bishop 2.5 
F/Sep 8  Decision Trees (and information theory) slides  Decision Trees by N. Nilsson, Bishop 1.6 Bishop 14.4 KL and mutual info 
M/Sep 11  Basic Point Estimation (MLE and MAP) PDF slides  Bishop 2.1, Appendix B, MLMath covariance 
background  Gaussians  Bishop 1.2.4, Bishop 2.3.12.3.3 
W/Sep 13  Regression, Regression slides  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 
F/Sep 15  Classification Logistic Regression, Naive Bayes slides
 Bishop 4.0, 4.2 4.5 
M/Sep 18  NB vs. LR, Basis Functions, RBFs  Naive Bayes vs Logistic Regression, short video: RBFs 
W/Sep 20  Overfitting and Regularization, Bias Variance Decomposition, Bias/Variance for Regression  Bishop 1.3, 1.5, 3.2 applet 
F/Sep 22  regression penalty slides Stepwise, streamwise, stagewise  short video:stepwise regression,Hastie et al. 7.17.3 LASSO (supplemental reading) 
M/Sep 25  MDL slides, MDL feature selection review  MDLsupplemental MDLbackground 
W/Sep 27  Neural Net slides Supervised Deep Networks  supplemental: deep net tutorial 
F/Sep 29  more deep learning Supervised Deep Networks  supplemental: gay learning 
M/Oct 2  Constrained Optimization and Lagrangian Duality  
W/Oct 4  Support Vector Machines
 Additional notes, Bishop 7.1 (Max Margin), Hearst 1998 
F/Oct 6  No class: Fall Break  
M/Oct 9  Kernel Methods
 Additional notes, Bishop 6.1,6.2 (Kernels) Supplemental: more on kernels 
W/Oct 11  Online Learning and Perceptron  
F/Oct 13  Boosting  Additional notes, Bishop 14.3, Schapire’s Tutorial

M/Oct 16  Performance Measures  
T/Oct 17  review session 5:00 p.m. ANNS Room: 110  Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, CrossValidation, Boosting Complexity consistency 
W/Oct 18  Midterm recitation recording  sample exam questions and answers more examples are part of the old final exam below 
F/Oct 20  No class  
M/Oct 23  Learning Theory?  
W/Oct 25  Vectors, Matrices, Eigenvectors  Kosecka’s review slides 
F/Oct 27  Dim. Reduction PCA eigenwords  Bishop Appendix C Properties of Matrices 
extras  PCR, PLS and CCA slides more courses  Bishop 12.1 supplemental slides 
M/Oct 30  Unsupervised Deep Networks 
W/Nov 1  Unsupervised Learning: Clustering, Kmeans EM  Bishop 9.19.3 
F/Nov 3  EM Generative PCA  Bishop 12.1–12.3, supplemental:Neal and Hinton 
extras  LDA slides  supplemental:LDA intro and original LDA paper 
not covered  Generative model summary, loss functions and ML speed slides  
M/Nov 6  Netflix  supplemental:netflix 
W/Nov 8  Project overview and advice and project slides; Real world machine learning  More advice and python for converting text to ints 
F/Nov 10  Bayesian Networks  supplemental: Koller+al, Graphical Models in a Nutshell 
M/Nov 13  Markov Networks  supplemental: Koller+al, Graphical Models in a Nutshell 
W/Nov 15  Exact Inference: Junction Tree Algorithm  supplemental: Koller+al, Graphical Models in a Nutshell 
F/Nov 17  Hidden Markov Models  Rabiner’s HMM Tutorial, supplemental:Bishop 13.1–2 
M/Nov 20  Structured Prediction?  
W/Nov 22  No class  
F/Nov 24  No class — Happy Thanksgiving!!  
M/Nov 27  SemiSupervised Learning, Active Learning?  
W/Nov 29  TBD  
F/Dec 1  TBD  
M/Dec 4  Reinforcement Learning?  
W/Dec 6  Big Data  Unreasonable effectiveness of data 
F/Dec 8  The Future of ML and humanity  
M/Dec 11  Final project awards; Final Review Other material Review Questions
 LIONbook  a quick review; After CIS520 
W/Dec 13  Review Session: 4:00–5:00 pm Building: TBD  
T/Dec 19  Final: 9:00am11:00am Location TBD  2014 final and solution 