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

Lectures will change; Midterm and final dates will not

DateSubjectReadingQuestions
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 infoquiz
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, 3.3.2pre-quiz
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 scale_invariancequiz
W/Sep 19regression penalty slides Stepwise, streamwise, stagewiseshort video:stepwise regression,Hastie et al. 7.1-7.3 LASSO (supplemental reading)quiz
F/Sep 21MDL (pdf)MDL-supplemental MDL-backgroundquiz
M/Sep 24Classification Logistic Regression, Naive Bayes slides
Bishop 4.0, 4.2- 4.5quiz
W/Sep 26Neural Net slides Supervised Deep NetworksCNN description supplemental: deep net tutorialquiz
F/Sep 28more deep learning Supervised Deep Networkssupplemental: gay learning 
M/Oct 1Optimization,(pdf),kernels (pdf)Constrained Optimizationquiz
W/Oct 3Support Vector Machines (pdf)
Additional notes, Bishop 7.1 (Max Margin), Hearst 1998 
F/Oct 5 No class: Fall Break   
M/Oct 8Perceptrons (pdf)Online Learning and Perceptronsquiz
W/Oct 10Boosting gradient boostingBoosting,Bishop 14.3, Schapire’s Tutorial
 
F/Oct 12Midterm Review (pdf)Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, Cross-Validation, Boosting Complexity consistency 
M/Oct 15 Midterm Sample questions 2016,2017 and answers 2016,2017.quiz
W/Oct 17SVD SVD slidesKosecka’s review slidesquiz
F/Oct 19PCA,(pdf),eigenwordsBishop 12.1: PCA Bishop Appendix C Properties of Matrices PCR, PLS and CCAquiz
M/Oct 22Unsupervised Deep Networks quiz
W/Oct 24Unsupervised Learning: Clustering, K-means EMBishop 9.1-9.3quiz
F/Oct 26LDA slidesSupplemental:LDA intro and original LDA paperquiz
M/Oct 29 No class   
W/Oct 31Generative PCA, CCABishop 12.1–12.3, supplemental:Neal and Hintonsquiz
F/Nov 2Netflix,(pdf)supplemental:netflix 
M/Nov 5Project overview and advice and project slides; Real world ML loss functionsMore advice, supplemental:Performance Measures 
W/Nov 7Belief Nets (pdf)Jordan Bayes Net chapter, additional notes, supplemental: Koller paperquiz
F/Nov 9HMMs pdfHidden Markov Models supplemental: Rabiner’s HMM Tutorial, Bishop 13.1–2quiz
M/Nov 12Recurrent Nets (pdf) manifolds (pdf)RNNs; supplemental:LSTMs 
W/Nov 14Active Learning (pdf)Semi-Supervised and Active Learning active learning survey (sections 3.1–3.5 are the key ones; everything after that is supplementatl) 
F/Nov 16Experimental design (pdf), Missing data (pdf)  
M/Nov 19GANS, ML Overview  
W/Nov 21 No class   
F/Nov 23 No class — Happy Thanksgiving!!   
M/Nov 26Reinforcement Learningsupplemental: additional reinforcement learning exercise 
W/Nov 28Reinforcement Learningsupplemental: additional reinforcement learning exercise 
F/Nov 30Structured Predictionsupplemental: Structured Prediction 
M/Dec 3Causality?, Variable Importance  
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 ML Overview
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 Final: 9:00am-11:00am Chem 102 2016 final and solution 

Python code to download all course materials: scrape code

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Page last modified on 19 November 2018 at 12:17 PM