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Lectures: Wu & Chen Auditorium, Monday and Wednesday, 10:30am-noon
Recitations: Wu & Chen Auditorium, Friday, 9:30am-11:00am

Lectures will change; Midterm and final date will not

On your ownlearn linear algebraSelf Test, Self Test Solutions, Strang’s Course
W/Aug 27 Intro slides 
F/Aug 29Probability Review slidesBishop 1.1-1.4
M/Sep 1 No class: Labor Day  
W/Sep 3 Nearest NeighborBishop 2.5
F/Sep 5Intro to Matlab TutorialCoursera octave tutorial
M/Sep 8 Decision Trees (and information theory) slidesDecision Trees by N. Nilsson, Bishop 1.6, 14.4
W/Sep 10 Basic Point Estimation (MLE andMAP) pdf slidesBishop 2.1, Appendix B, MLMath
F/Sep 12Gaussians,1-D Regression slidesBishop 1.2.4, 2.3.1–2.3.3 optional slides
M/Sep 15 RegressionBishop 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 RegularizationBishop 1.3, 1.5, 3.2 applet
F/Sep 19 Bias/Variance for Regression, slidesHastie et al. 7.1-7.3 (supplemental reading:LASSO)
M/Sep 22 Classification Naive Bayes slides
W/Sep 24 Logistic Regression slidesNaive Bayes vs Logistic Regression, Bishop 4.0, 4.2- 4.5
F/Sep 26 NB vs. LR, Basis Functions RBF slides 
M/Sep 29Feature Selection and MDL slidesHastie et al. 7.5-7.8
W/Oct 1 more feature selection slidesconsistency
F/Oct 3BoostingBishop 14.3, Schapire’s Tutorial
M/Oct 6 Neural Net slides Deep Networks 
M/Oct 6 review session 5:00 p.m. Wu & ChenRecitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, Cross-Validation, Boosting
W/Oct 8Midtermsample 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 Dualitylagrange examples Bishop Appendix E LagrangeMultipliers
F/Oct 17Support Vector Machines
Bishop 7.1 (Max Margin) Hearst 1998
M/Oct 20 SVM slides
Burges 1998
W/Oct 22 perceptron slidessupplemental reading:MIRA, Perceptrons and SVM Recap and Optimization
F/Oct 24Vectors, Matrices, EigenvectorsKosecka’s review slides
M/Oct 27 Dim. Reduction PCABishop Appendix C Properties of Matrices
W/Oct 29 PCR and CCABishop 12.1 supplemental slides
F/Oct 31 Deep Networkssupplemental:autoencoders
M/Nov 3 Unsupervised Learning: Clustering, K-meansBishop 9.1-9.3
W/Nov 5 EM
Bishop 12.1–12.3, supplemental:Neal and Hinton
F/Nov 7 EM and LDA slidessupplemental:LDA intro and original LDA paper
M/Nov 10 LDA slides, generative model summary kernel summary 
W/Nov 12 Netflixsupplemental:netflix
F/Nov 14Project Overview and Advice and project slides; Real world machine learning and precision/recallMore Advice and python for converting text to ints
M/Nov 17 Bayes Netssupplemental: Koller+al, Graphical Models in a Nutshell
W/Nov 19 Bayes Nets slidesBishop 8.2
F/ Nov 21Inference in Bayes Nets ML speed slides 
M/Nov 24 Hidden Markov Models and slidesRabiner’s HMM Tutorial supplemental:Bishop 13.1–2
W/Nov 26 No class — Happy Thanksgiving!! 
F/Nov 28No class — Happy Thanksgiving!! 
M/Dec 1 spectral Hidden Markov Models 
W/Dec 3 Causality and Active learning 
F/Dec 5Big Datapaper
M/Dec 8Final 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: 9:00am-11:00am COHN G17 last year’s final and solution
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Page last modified on 15 December 2014 at 10:55 AM