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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 date will not

On your ownlearn linear algebra, basic probabilitySelf Test, Self Test Solutions, Strang’s Course Probability Review Bishop 1.1-1.4, slides
W/Aug 30Intro, intro_slides 
F/Sep 1Intro to Matlab TutorialCoursera octave tutorial
M/Sep 4 No class: Labor Day  
W/Sep 6Local learning Norm slidesBishop 2.5
F/Sep 8Decision Trees (and information theory) slidesDecision Trees by N. Nilsson, Bishop 1.6 Bishop 14.4 KL and mutual info
M/Sep 11Basic Point Estimation (MLE and MAP) PDF slidesBishop 2.1, Appendix B, MLMath covariance
backgroundGaussiansBishop 1.2.4, Bishop 2.3.1-2.3.3
W/Sep 13Regression, 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 15Classification Logistic Regression, Naive Bayes slides
Bishop 4.0, 4.2- 4.5
M/Sep 18NB vs. LR, Basis Functions, RBFsNaive Bayes vs Logistic Regression, short video: RBFs
W/Sep 20Overfitting and Regularization, Bias Variance Decomposition, Bias/Variance for RegressionBishop 1.3, 1.5, 3.2 applet
F/Sep 22regression penalty slides Stepwise, streamwise, stagewiseshort video:stepwise regression,Hastie et al. 7.1-7.3 LASSO (supplemental reading)
M/Sep 25MDL slides, MDL feature selection reviewMDL-supplemental MDL-background
W/Sep 27Neural Net slides Supervised Deep Networkssupplemental: deep net tutorial
F/Sep 29more deep learning Supervised Deep Networkssupplemental: gay learning
M/Oct 2Constrained Optimization and Lagrangian Duality 
W/Oct 4Support Vector Machines
Additional notes, Bishop 7.1 (Max Margin), Hearst 1998
F/Oct 6 No class: Fall Break  
M/Oct 9Kernel Methods
Additional notes, Bishop 6.1,6.2 (Kernels) Supplemental: more on kernels
W/Oct 11Online Learning and PerceptronAdditional notes
F/Oct 13BoostingAdditional notes, Bishop 14.3, Schapire’s Tutorial
M/Oct 16Performance Measures 
T/Oct 17review session 5:00 p.m. ANNS Room: 110Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, Cross-Validation, Boosting Complexity consistency
W/Oct 18 Midterm recitation recordingsample exam questions and answers more examples are part of the old final exam below
F/Oct 20 No class  
M/Oct 23Learning Theory: A Brief Primer 
W/Oct 25Vectors, Matrices, SVD SVD slidesKosecka’s review slides
F/Oct 27Dim. Reduction PCA eigenwordsBishop 12.1: PCA Bishop Appendix C Properties of Matrices PCR, PLS and CCA
M/Oct 30Unsupervised Deep Networks more courses
W/Nov 1Unsupervised Learning: Clustering, K-means EMBishop 9.1-9.3
F/Nov 3EM Generative PCA,LDA slidesBishop 12.1–12.3, supplemental:Neal and Hintonsupplemental:LDA intro and original LDA paper
M/Nov 6Netflixsupplemental:netflix
W/Nov 8Project overview and advice and project slides; Real world machine learningMore advice and python for converting text to ints
F/Nov 10Hidden Markov Modelssupplemental: Rabiner’s HMM Tutorial, Bishop 13.1–2
M/Nov 13Hidden Markov Modelssupplemental: Rabiner’s HMM Tutorial, Bishop 13.1–2
W/Nov 15Bayesian NetworksJordan book draft Ch 2 (Sec 2.1), additional notes, supplemental: Koller et al., Graphical Models in a Nutshell
F/Nov 17Markov Networks; Variable EliminationJordan book draft Ch 2 (Sec 2.2) and Ch 3; supplemental: Koller et al., Graphical Models in a Nutshell
M/Nov 20Sum-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
W/Nov 22 No class  
F/Nov 24 No class — Happy Thanksgiving!!  
M/Nov 27Structured 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)
W/Nov 29Semi-Supervised Learning, Active Learningsupplemental: Nigam et al., 2000 (EM for semi-supervised learning), Zhu’s semi-supervised learning survey, Settles’ active learning survey
F/Dec 1More deep learning, Killian’s lecture 
M/Dec 4Reinforcement Learningsupplemental: additional reinforcement learning exercise
W/Dec 6Big DataUnreasonable effectiveness of data
F/Dec 8The Future of ML and humanitysupplemental: job futures
M/Dec 11Final project awards; Other material Dynamic Nets current deep learning
LIONbook - a quick review; After CIS520
W/Dec 13 Review Session: 4:00–5:00 pm Building: ANNS 110Final Review Review Questions audio recording (under canvas announcements)
T/Dec 19 Final: 9:00am-11:00am CHEM 102 2014 final and solution and 2016 final and solution

Python code to download all course materials: scrape code

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Page last modified on 18 May 2018 at 02:02 PM