Course Activities 
Week 1 (Aug 2127) 
Get Familiar with Software: Intrudction to R 
R Brief Intro, R Guide For Reginners 

Read Chapter 1: Overview of Data Mining 
Lecture 1: Introduction 

Supplementary Reading: Data mining and statistics:
what is the connection? Friedman (1997) 
Homework 1.
Assigned on Aug 22, due on Sep 4. 
Week 2 (Aug 28Sep 3) 
Read Chapter 2: Theory of Supervised Learning 
Lecture 2: Statistical Decision Theory (I) 


Lecture 3: Statistical Decision Theory (II) 
Week 3 (Sep 4  Sep 10) 
Read Chapter 4.24.4: Linear Classificaton Methods for Binary Problems 
Lecture 4: Binary Classification (I): Basics 


Homework 2 Assignment. Assigned on Sep 5, due on Sep 19. 


Homework 2 Solution, Code 
Week 4 (Sep 11  Sep 17) 
Supplementary Reading: Choosing Between Logistic Regression and Discriminant Analysis, Press, S. and Wilson, S. (1978) 
Lecture 5: Binary Classification (II): Logistic Regression and Discriminant Analysis 

Curse of Dimensionality; Linear Binary Classification for High Dimensional Problems 
Lecture 6: Binary Classification (III): Extension to High Dimensional Classification Problems 
Week 4 (Sep 18  Sep 24) 
Read Chapter 4.1: Nonlinear Classification Methods 
Lecture 7: K nearest neighbor (Knn) methods 

Topic: Introduction to Multiclass Classifiction 
Lecture 8: Multiclass Classification 


Homework 3 Assignment.Assigned on Sep 19, due on Oct 3.
Solution, Code 
Week 5 (Sep 25  Oct 1) 
Topic: Nonlinear Discriminant Analysis 
Lecture 9: QDA and RDA 

Supplementary Reading: LDA for improved large vocabulary continuous speech recognition 
Lecture 10: PCA 
Week 6 (Oct 2  Oct 8) 
Topic: Linear Regression Models 
Lecture 11: Linear Regression 

Read Chapter 3 : Linear Regression, Supplementary Reading: Linear Model Theory 
Homework 4 Assignment, Solution, Code.
Assigned on Oct 3, due on Oct 24 Solution, Code 
Week 7 (Oct 9  Oct 15) 
Read Chapter 3 : Variable Selection for Linear Regression 
Lecture 12: Variable Selection (I) 

Reading:Regression Shrinkage and Selection via the LASSO, 

Week 8 (Oct 16  Oct 22) 
Final Project: Project assigned on Oct 19th, due on Dec 12 
Lecture 13: Shrinkage Methdods by LASSO


Final Project: Suggested Reading List 

Week 9 (Oct 23  Oct 29) 
Supplementary Reading: Regularization and variable selection via the
elastic net 
Homework 5, Prostate data set,
data info. Assigned on Oct 24th, due on Nov 14.Solution, Code



Lecture 14: Beyond LASSO,
Lecture 14+: Functional ANOVA 
Week 10 (Oct 30  Nov 5) 
Read Chapter 8 : Model Tuning and Evaluation 
Lecture 15: Model Selection and Assessment


Supplenmentary Reading: Leaveoutone Cross Validation 

Week 11 (Nov 6  Nov 12) 
Read Chapter 4 (4.5) 
Lecture 16: Modern Classification vis Separting Hyperplanes 

Read Chapter 12 
Lecture 17: Support Vector Machines 
Week 12 (Nov 13  19) 
Supplementary Reading: The Entire Regularization Path for the Support Vector Machine 
Lecture 18: Multiclass Support Vector Machines 


Homework 6, assigned on Nov 16, due on Dec 5 .

Week 13 (Nov 20  26) 
Read Chapter 9 (9.2) : Treebased Methods 
Lecture 19: Optimization Programming 


Lecture 20: Classification and Regression Trees 
Week 14 (Nov 27  Dec 3) 
Read Chapter 8.7 : Bootstrap and Bagging 
Lecture 21: Bagging and Boost 

Supplenmentary Reading: Explaining Adaboost 

Week 15 (Dec 4  Dec 10) 
Read Chapter 14 (14.114.4) : Unsupervised Learning 
Lecture 22: Cluster Analysis 