#!/usr/local/bin/php CIS520 Machine Learning | Lectures / Lectures
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Lectures: Wu & Chen Auditorium, Monday and Wednesday, 10:30am-noon
Recitations: Wu & Chen Auditorium, Friday, 9:30am-11:00am
2016 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 31 Intro, intro_slides 
F/Sep 2Probability ReviewBishop 1.1-1.4, slides
M/Sep 5 No class: Labor Day  
W/Sep 7Nearest Neighbor norm slidesBishop 2.5
F/Sep 9Intro to Matlab TutorialCoursera octave tutorial
M/Sep 12 Decision Trees (and information theory) slidesDecision Trees by N. Nilsson, Bishop 1.6 Bishop 14.4
W/Sep 14 Basic Point Estimation (MLE andMAP) PDF slidesBishop 2.1, Appendix B, MLMath covariance
F/Sep 16Gaussians,1-D Regression slidesBishop 1.2.4, Bishop 2.3.1-2.3.3 optional slides
M/Sep 19 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 21 Overfitting and Regularization, Bias Variance Decomposition, Bias/Variance for RegressionBishop 1.3, 1.5, 3.2 applet
F/Sep 23 regression penalty slides Stepwise, streamwise, stagewiseHastie et al. 7.1-7.3 (supplemental reading:LASSO)
M/Sep 26 Classification Naive Bayes slides
W/Sep 28 Logistic RegressionNaive Bayes vs Logistic Regression, Bishop 4.0, 4.2- 4.5
F/Sep 30 NB vs. LR, Basis Functions, RBFs robust regressionshort videos on hat matrix and stepwise regression and RBFs
M/Oct 3MDL slides, MDL feature selection reviewHastie et al. 7.5-7.8
W/Oct 5 BoostingBishop 14.3 , Schapire’s Tutorial
F/Oct 7 No class: Fall Break  
M/Oct 10 Neural Net slides Supervised Deep Networkssupplemental: deep net tutorial
W/Oct 12more deep learning 
R/Oct 13review session 5:00 p.m. meyerson Room: B(asement)Recitation Remix MLE/MAP examples (Multivariate Gaussian, Poisson) Decision Trees, Cross-Validation, Boosting Complexity consistency
F/Oct 14 Midterm sample exam questions and answers more examples are part of the old final exam below
M/Oct 17 Kernel Methods kernel examples kernel regression
Bishop 6.1,6.2 (Kernels) Supplemental: more on kernels
W/Oct 19 More Kernels, Lagrange Duality Lagrange examplesBishop Appendix E LagrangeMultipliers video working an example
F/Oct 21Support Vector Machines
Bishop 7.1 (Max Margin) Hearst 1998
M/Oct 24 SVM slides
Burges 1998
W/Oct 26 Perceptron slidessupplemental reading:MIRA, Perceptrons and SVM Recap and Optimization
F/Oct 28Vectors, Matrices, EigenvectorsKosecka’s review slides
M/Oct 31 Dim. Reduction PCA eigenwordsBishop Appendix C Properties of Matrices
W/Nov 2 Unsupervised Deep Networks 
F/Nov 4 PCR, PLS and CCA slides more coursesBishop 12.1 supplemental slides
M/Nov 7 Unsupervised Learning: Clustering, K-means EMBishop 9.1-9.3
W/Nov 9 EM Generative PCABishop 12.1–12.3, supplemental:Neal and Hinton
F/Nov 11 LDA slidessupplemental:LDA intro and original LDA paper
M/Nov 14 Generative model summary, loss functions and ML speed slides 
W/Nov 16 Netflixsupplemental:netflix
F/Nov 18Project overview and advice and project slides; Real world machine learningMore advice and python for converting text to ints
M/Nov 21 Bayes Netssupplemental: Koller+al, Graphical Models in a Nutshell
W/Nov 23 no class  
F/Nov 25 No class — Happy Thanksgiving!!  
M/Nov 28 Bayes Net constructionBishop 8.2
W/ Nov 30Bayes Net Inference Hidden Markov Models and HMM slidesRabiner’s HMM Tutorial, supplemental:Bishop 13.1–2
F/Dec 2 Recurrent Neural Networks 
M/Dec 5 Active learning and Causality 
W/Dec 7 Big DataUnreasonable effectiveness of data
F/Dec 9The Future of ML and humanity 
M/Dec 12Final project awards; Final Review Other material Review Questions
LIONbook - a quick review; After CIS520
W/Dec 21 Review Session: 4:00–5:00 pm Building: DRLB Room: A1  
Th/Dec 22 Final: 9:00am-11:00am FAGN AUD(in the Nursing school!!) 2014 final and solution
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Page last modified on 12 July 2018 at 05:17 PM