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Lectures: Wu & Chen Auditorium, Monday and Wednesday, 9:30am-11:00am
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 28 Intro slides 
F/Aug 30Probability Review slidesBishop 1.1-1.4
M/Sep 2 No class: Labor Day  
W/Sep 4 Nearest NeighborBishop 2.5
F/Sep 6Intro to Matlab TutorialCoursera octave tutorial
M/Sep 9 Decision Trees (and information theory) slidesDecision Trees by N. Nilsson, Bishop 1.6, 14.4
W/Sep 11 Basic Point Estimation (MLE andMAP) pdf slidesBishop 2.1, Appendix B, MLMath
F/Sep 13Gaussians,1-D Regression slidesBishop 1.2.4, 2.3.1–2.3.3 optional slides
M/Sep 16 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 18 Bias Variance DecompositionBishop 1.3, 1.5, 3.2 applet
F/Sep 20 Bias/Variance for Regression, Overfitting and Regularization slidesHastie et al. 7.1-7.3 (supplemental reading:LASSO)
M/Sep 23 Classification Naive Bayes slides
W/Sep 25 Logistic Regression slidesNaive Bayes vs Logistic Regression, Bishop 4.0, 4.2- 4.5
F/Sep 27 NB vs. LR, Basis Functions 
M/Sep 30Feature Selection and MDL slidesHastie et al. 7.5-7.8
W/Oct 2 more feature selection slides 
F/Oct 4BoostingBishop 14.3, Schapire’s Tutorial
M/Oct 7 Neural Net slides 
M/Oct 7 review session 7:00 p.m. DRLB A8Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, Cross-Validation, Boosting
W/Oct 9Midterm 
F/Oct 11 No class: Fall Break Please take survey
M/Oct 14 Kernel Methods
Bishop 6.1,6.2 (Kernels)
W/Oct 16 More Kernels, Lagrange DualityBishop Appendix E LagrangeMultipliers
F/Oct 18Support Vector Machines
Bishop 7.1 (Max Margin) Hearst 1998
M/Oct 21 SVM slides
Burges 1998
W/Oct 23 perceptron slidessupplemental reading:MIRA, Perceptrons and SVM Recap and Optimization
F/Oct 25Vectors, Matrices and Tensors; EigenvectorsKosecka’s review slides
M/Oct 28 eigenvectors, SVDBishop Appendix C Properties of Matrices
W/Oct 30 Dim. Reduction PCABishop 12.1
F/Nov 1 PCR and CCAsupplemental slides
M/Nov 4 Deep Networkssupplemental:autoencoders
W/Nov 6 Unsupervised Learning: Clustering, K-meansBishop 9.1-9.3
F/Nov 8 EM
Bishop 12.1–12.3, supplemental:Neal and Hinton
M/Nov 11 LDA slidessupplemental:LDA intro and original LDA paper
W/Nov 13 Netflixsupplemental:netflix
F/Nov 15Project Overview and Advice and slides; Real world machine learningMore Advice
M/Nov 18 Bayes Netssupplemental: Koller+al, Graphical Models in a Nutshell
W/Nov 20 Bayes NetsBishop 8.2
F/ Nov 22Bayes Nets 
M/Nov 25 Inference in Bayes Nets Hidden Markov ModelsRabiner’s HMM Tutorial supplemental:Bishop 13.1–2
W/Nov 27 No class — Happy Thanksgiving!! 
F/Nov 29No class — Happy Thanksgiving!! 
M/Dec 2 spectral Hidden Markov Models 
W/Dec 4 Causality and Active learning 
F/Dec 6Big Datapaper
M/Dec 9Final project awards; Final Review Other material
LIONbook - a quick review; After CIS520
M/Dec 16 Review Session: 4:00–5:00Skirkanich Hall Berger Auditorium
W/Dec 18 Final! 9:00am-11:00am Dhirubhai Ambani Auditorium (JMHH G06) this year’s final and solution
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Page last modified on 03 January 2014 at 09:43 AM