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Lectures: Wu & Chen Auditorium, Monday and Wednesday, 10:30am-noon, Friday, 9:30am-11:00am
See canvas for lecture recordings.

Lectures will change; Midterm and final dates will not

On your ownreview probabilityProbability Review Bishop 1.1-1.4, slides MIT Probability Open Course  
On your ownreview linear algebraStrang’s MIT OpenCourse MLMath Self Test, Self Test Solutions,
W/Aug 29Intro, (pdf)  
F/Aug 31Point Estimation: MLE and MAP probability densities (pdf)supplemental:Bishop 2.1, Appendix B, covariancequiz
M/Sep 3 No class: Labor Day   
W/Sep 5Local learning Norms (pdf)Bishop 2.5quiz
F/Sep 7Intro to Matlab TutorialCoursera octave tutorialquiz
M/Sep 10Decision Trees and information theory (pdf)Decision Trees by N. Nilsson, Bishop 1.6 Bishop 14.4 KL and mutual info 
on your ownGaussiansBishop 1.2.4, Bishop 2.3.1-2.3.3 
W/Sep 12Regression, Regression slidesBishop 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 
F/Sep 14RBF, gradient descent quiz
M/Sep 17Overfitting and Regularization, Bias Variance Decomposition, Bias/Variance for RegressionBishop 1.3, 1.5, 3.2 applet 
W/Sep 19regression penalty slides Stepwise, streamwise, stagewiseshort video:stepwise regression,Hastie et al. 7.1-7.3 LASSO (supplemental reading) 
F/Sep 21MDL (pdf)MDL-supplemental MDL-background 
M/Sep 24Classification Logistic Regression, Naive Bayes slides
Bishop 4.0, 4.2- 4.5 
W/Sep 26Neural Net slides Supervised Deep Networkssupplemental: deep net tutorial 
F/Sep 28more deep learning Supervised Deep Networkssupplemental: gay learning 
M/Oct 1Constrained Optimization and Lagrangian Duality  
W/Oct 3Support Vector Machines
Additional notes, Bishop 7.1 (Max Margin), Hearst 1998 
F/Oct 5 No class: Fall Break   
M/Oct 8Online Learning and PerceptronAdditional notes 
W/Oct 10BoostingAdditional notes, Bishop 14.3, Schapire’s Tutorial
F/Oct 12Review for midtermRecitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, Cross-Validation, Boosting Complexity consistency 
M/Oct 15 Midterm Sample questions and answers. More examples are part of the old final exam below. 
W/Oct 17SVD SVD slidesKosecka’s review slides 
F/Oct 19Dim. Reduction PCA eigenwordsBishop 12.1: PCA Bishop Appendix C Properties of Matrices PCR, PLS and CCA 
M/Oct 22Unsupervised Deep Networks  
W/Oct 24Unsupervised Learning: Clustering, K-means EMBishop 9.1-9.3 
F/Oct 26LDA slidesSupplemental:LDA intro and original LDA paper 
M/Oct 29Performance Measures  
W/Oct 31Generative PCA, CCABishop 12.1–12.3, supplemental:Neal and Hintons 
F/Nov 2Netflixsupplemental:netflix 
M/Nov 5Project overview and advice and project slides; Real world machine learningMore advice 
W/Nov 7Bayesian NetworksJordan book draft Ch 2 (Sec 2.1), additional notes, supplemental: Koller et al., Graphical Models in a Nutshell 
F/Nov 9Markov Networks; Variable EliminationJordan book draft Ch 2 (Sec 2.2) and Ch 3; supplemental: Koller et al., Graphical Models in a Nutshell 
M/Nov 12Hidden Markov Modelssupplemental: Rabiner’s HMM Tutorial, Bishop 13.1–2 
F/Nov 16More deep learning, Dynamic NetsKillian’s lecture 
M/Nov 19Sum-Product, Max-SumJordan book draft Ch 4 (Sec 4.1, 4.3), supplemental: Jordan book draft Ch 17, Koller et al., Graphical Models in a Nutshell 
M/Nov 19Semi-Supervised Learning, Active Learningsupplemental: Nigam et al., 2000 (EM for semi-supervised learning), Zhu’s semi-supervised learning survey, Settles’ active learning survey 
W/Nov 21 No class   
F/Nov 23 No class — Happy Thanksgiving!!   
M/Nov 26Reinforcement Learningsupplemental: additional reinforcement learning exercise 
M/Dec 3Reinforcement Learningsupplemental: additional reinforcement learning exercise 
F/Nov 30Structured Predictionsupplemental: Lafferty et al., 2001 (CRFs), McCallum, 2003 (more CRFs), Taskar et al., 2003 (StructSVM), Collins et al., 2008 (EG algorithms for CRFs and StructSVM), Joachims et al., 2009 (Cutting-plane algorithm for StructSVM) 
M/Dec 3Causality  
W/Dec 5Big DataUnreasonable effectiveness of data 
F/Dec 7The Future of ML and humanitysupplemental: job futures 
M/Dec 10Final project awards; Other material current deep learning
LIONbook - a quick review; After CIS520 
?T/Dec 11???? Review Session: 5:00–6:00 pm Building: Towne 100 Final Review Review Questions 
F/Dec 14 -but registrar says “tentative” Final: 9:00am-11:00am TBD 2016 final and solution 

Python code to download all course materials: scrape code

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Page last modified on 20 September 2018 at 05:46 PM