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Lectures: Wu & Chen Auditorium, Monday and Wednesday, 10:30am-noon
Recitations: Wu & Chen Auditorium, Friday, 9:30am-11:00am
2014 Videorecordings
2015 Videorecordings
Python code to download all course materials: scrape code

Lectures will change; Midterm and final date will not

On your ownlearn linear algebraSelf Test, Self Test Solutions, Strang’s Course
W/Aug 26 Intro slides 
F/Aug 28Probability ReviewBishop 1.1-1.4, slides
M/Aug 31Nearest NeighborBishop 2.5
W/Sep 2 Decision Trees (and information theory) slidesDecision Trees by N. Nilsson, Bishop 1.6 Bishop 14.4
F/Sep 4Intro to Matlab TutorialCoursera octave tutorial
M/Sep 7 No class: Labor Day  
W/Sep 9 Basic Point Estimation (MLE andMAP) pdf slidesBishop 2.1, Appendix B, MLMath covariance
F/Sep 11Gaussians,1-D Regression slidesBishop 1.2.4, Bishop 2.3.1-2.3.3 optional slides
M/Sep 14 Regression more_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 16 Bias Variance Decomposition Overfitting and RegularizationBishop 1.3, 1.5, 3.2 applet
F/Sep 18 Bias/Variance for Regression, regression penalty slides Stepwise, streamwise, stagewiseHastie et al. 7.1-7.3 (supplemental reading:LASSO)
M/Sep 21 Classification Naive Bayes slides
W/Sep 23 Logistic RegressionNaive Bayes vs Logistic Regression, Bishop 4.0, 4.2- 4.5
F/Sep 25 No class; Pope in town :( NB vs. LR, Basis Functions RBF slides and video, short videos on hat matrix and stepwise regression 
M/Sep 28MDL slidesHastie et al. 7.5-7.8
W/Sep 30 BoostingBishop 14.3 , Schapire’s Tutorial
F/Oct 2feature selection reviewconsistency
M/Oct 5 Neural Net slides Supervised Deep Networkssupplemental: deep net tutorial
T/Oct 6review session 5:00 p.m. Heilmeier HallRecitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, Cross-Validation, Boosting |Complexity
W/Oct 7Midtermsample exam questions and answers
F/Oct 9 No class: Fall Break  
M/Oct 12 Kernel Methods kernel examples
Bishop 6.1,6.2 (Kernels) Supplemental: more on kernels
W/Oct 14 More Kernels, Lagrange Duality Lagrange examplesBishop Appendix E LagrangeMultipliers
F/Oct 16Support Vector Machines
Bishop 7.1 (Max Margin) Hearst 1998
M/Oct 19 SVM slides
Burges 1998
W/Oct 21 perceptron slidessupplemental reading:MIRA, Perceptrons and SVM Recap and Optimization
F/Oct 23Vectors, Matrices, EigenvectorsKosecka’s review slides
M/Oct 26 Dim. Reduction PCA eigenwordsBishop Appendix C Properties of Matrices
W/Oct 28 Unsupervised Deep Networks 
F/Oct 30 PCR, PLS and CCA slidesBishop 12.1 supplemental slides
M/Nov 3 Unsupervised Learning: Clustering, K-means EMBishop 9.1-9.3
W/Nov 4 EM generative PCABishop 12.1–12.3, supplemental:Neal and Hinton
F/Nov 6 LDA slidessupplemental:LDA intro and original LDA paper
M/Nov 9 generative model summary, loss functions and ML speed slides 
W/Nov 11 Netflixsupplemental:netflix
F/Nov 13Project Overview and Advice and project slides; Real world machine learningMore Advice and python for converting text to ints
M/Nov 16 Bayes Netssupplemental: Koller+al, Graphical Models in a Nutshell
W/Nov 18 Bayes Nets slides QuestionsBishop 8.2
F/ Nov 20Inference in Bayes Nets Hidden Markov Models and HMM slidesRabiner’s HMM Tutorial supplemental:Bishop 13.1–2
M/Nov 23 Recurrent Neural Networks 
W/Nov 25 no class 
F/Nov 27No class — Happy Thanksgiving!! 
M/Nov 30 Active learning and Causality 
W/Dec 2 BIg Data slidesUnreasonable effectiveness of data, supplemental:sampling
F/Dec 4The Future of ML and humanity 
M/Dec 7Final project awards; Final Review Other material Review Questions
LIONbook - a quick review; After CIS520
F/Dec 11 Review Session: 4:00–5:00 pm Wu & Chen  
M/Dec 14 Final: 9:00am-11:00am Towne 100 2013 final and solution and 2014 final and solution
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Page last modified on 18 January 2016 at 03:35 PM