1/27 MLR & KNN M48, MLR and KNN, More Simple Generalities Handout, KJ Ch5&Sec. 6.2&7.4, JWHT Sec. 3.5&6.1, HTF Sec. 2.3

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1 Stat 502X Details-2016 N=Stat Learning Notes, M=Module/Stat Learning Slides, KJ=Kuhn and Johnson, JWHT=James, Witten, Hastie, and Tibshirani, HTF=Hastie, Tibshirani, and Friedman /11 Intro M1, N Ch1, KJ Ch1-3, JWHT Ch1, HTF Sec /25 Kernel PC's, Kernels M4, M48, Kernel PC Example, Two Kernel Arguments Handout, KJ Pages , JWHT Sec , HTF Sec /1 Lasso, ENet, NNG, glmnet M5, JWHT Sec , ~hastie/glmnet/glmnet_a lpha.html 2/8 Regression Splines and MV Regression Splines (Tensor Bases and MARS) M10, KJ Sec. 7.2, HTF Sec wiki/multivariate_adapti ve_regression_splines 1/13 Decision Thy, Var- Bias Tradeoff, Cross- Validation M2, (M28), 2-Class Classification Handout, KJ Ch4, JWHT Ch2&Sec. 5.1&5.3, HTF Sec , Sec /20 SVD, Principal Components M3, M4, SVD Example, JWHT Sec /27 MLR & KNN M48, MLR and KNN, More Simple Generalities Handout, KJ Ch5&Sec. 6.2&7.4, JWHT Sec. 3.5&6.1, HTF Sec /3 PCR, PLS M7, KJ Sec. 6.3, JWHT Sec , HTF Sec /10 Some Computing for Linear Prediction (and KNN) KJ Sec. 6.5&7.5, JWHT Sec s/pls/vignettes/plsmanual.pdf 1/15 Gram-Schmidt, SVD M3, Gram-Schmidt&QR Handout, HTF Sec. 3.2&3.4 1/22 PC's, Kernel PC's M4, SVD Example, Kernel PC Example, JWHT Sec , HTF Sec /29 Linear Prediction, Ridge, Lasso M5, KJ Sec. 6.1&6.4, JWHT Sec. 6.2, HTF Sec , 3.4.2, /5 Haar Basis, Regression Splines M8, M9, JWHT Sec , HTF Sec. 5.9, 5.1&5.2 2/12 More Computing and 1-D Smoothing Splines M11, JWHT Sec. 7.5

2 Spring Break /15 Multivariate Smoothing Splines, 1-D Kernel Smoothing M13, JWHT Pages 23-24, M14, JWHT Sec /22 Neural Networks M16, KJ Sec. 7.1, HTF Sec /29 Bagging/Random Forests M18, JWHT Sec. 8.2, KJ Sec. 8.4&8.5, HTF Ch15 3/7 Classification M2, M28 3/21 Classification, KNN Classifiers M28, M45, JWHT Sec &4.4.1, Module 37 3/28 Logistic Regression Module 30, HTF Sec. 4.4, JWHT Sec. 4.3&4.5 2/17 Kerneland Local Mean and Linear Smoothing, Numerical Examples M14, Morris Example 2/24 Neural and Radial Basis Function Networks, Regression Trees HTF Sec. 6.7, M17, KJ Section 8.1, JWHT Sec. 8.1, HTF Sec /2 Random Forests/PRIM M19 3/9 Review Items of Course Summary e.edu/~vardeman/stat50 2x/Stat%20502X%20Sum mary.pdf 3/23 Linear Classification M29 and M30, JWHT Sec. 4.4, KJ Ch12, HTF Sec /30 Computing for KNN and Penalized Logistic Regression, Maximum Margin Classifiers Module 31, JWHT Sec. 9.1, K&J Sec /19 MARS Computing, Low-D Smoothing in High-D (Additive and Other Models for Big p) M15, JWHT Sec. 7.7&Sec 7.8.3, HTF Sec. 9.1, /26 Regression Trees, Bagging ges/rpart/vignettes/lon gintro.pdf M20, JWHT Sec , HTF Sec /4 Combining Predictors/Ensembles/ Gaussian Process Predictors and RBF Networks M21, JWHT Sec , KJ Sec. 8.6, M25 3/25 Organizing for a Large Predictive Analytics Project (Ian Mouzon) 4/1 Kaggle, Support Vector Classifiers, Support Vector Machines M32, M33, JWHT Sec , "Handouts"

3 /4 More Comments on SVM's, Classification Trees and Random Forests, Prototype Methods M17, M18 and M37 4/11 Model-Based Clustering, Clustering (Graphical) Spectral Features M40, M42 4/18 Association Rules and Market Basket Analysis M38, Problem 29 Stat 602X HW Spring /6 AdaBoostM.1 M35, Problem 3 Stat 602X Exam 2 Sp'11 4/13 Multi-Dimensional Scaling, Self-Organizing Maps, Features for Text Processing M41, M40, M47, Problem 9, Stat 602X Exam 2 Sp'13 4/20 Deep Learning (Boltzmann Machines) M49 4/8 Kaggle, Neural Nets and Classification, K- Means Clustering, Hierarchical Clustering M39, JWHT Sec /15 Kaggle, Features for Text Processing, Kernel Mechanics M48 4/22 Kaggle, XGBoost M36, XGBoost web pages 15 4/25 Sparse PC's, Sparse Bayes Predictors M43, M50 4/27 AUC Argument, Google Page Ranks M46 4/29 Kaggle, More on SVMs

4 9 3/7 3/9 8 9 Spring Break /3 3/5 3/7 Regression Trees/Random Forests M20, M18, JWHT Sec. 8.2, KJ Sec. 8.4&8.5, HTF Ch15 3/10 Random Forests/PRIM M19 3/24 Combining Predictors/Classification M28 3/31 Linear Classification HTF Sec /7 Support Vector Machines and R M32, M33, JWHT Sec , 9.6 4/14 Boosting Issues/Neural Nets, Prototypes, Nearest Neighbors, and Classification M37 3/12 Combining Predictors/Ensembles M21, JWHT Sec , KJ Sec /26 Classification M28, M45, JWHT Sec & /2 Linear Classification/Logistic Regression M30, HTF Sec. 4.4, JWHT Sec. 4.3&4.5 4/9 Support Vector Machines/AdaBoostM.1 M34, M35, Problem 3 Stat 602X Exam 2 Sp'11 4/16 Nearest Neighbor Classification/K-Means Clustering M39, JWHT Sec /28 Linear Classification M29, JWHT Sec. 4.4, KJ Ch12 4/4 Support Vector Machines M31, JWHT Sec. 9.1, K&J Sec /11 AdaBoostM.1/ Other Boosting M36, K&J Sec /18 Hierarchical Clustering/Spectral Clustering M /21 Model-Based Clustering and Multi- Dimensional Scaling M40, M41 4/23 Association Rules and Market Basket Analysis M38, Problem 29 Stat 602X HW Spring /25 Quantitative Features from Categorical Inputs/ Text Processing Features M28,M47

5 15 4/28 Text Processing Features/Undirected Graphical Models and Machine Learning M49 4/30 Undirected Graphical Models and Machine Learning 5/2 Course Summary