Course Activities 
Week 1 (Jan 913) 
Read Chapter 1: Overview of Data Mining 
Lecture 1: Introduction 

Get Familiar with Software: Intrudction to R 
R Brief Intro, R Guide For Reginners 

Supplementary Reading: Data mining and statistics:
what is the connection? Friedman (1997) 
Homework 1.
Assigned on Jan 15, due on Jan 29. 
Week 23 (Jan 1427) 
Read Chapter 2: Theory of Supervised Learning 
Lecture 2: Statistical Decision Theory (I) 


Lecture 3: Statistical Decision Theory (II) 
Week 4 (Jan 28Feb 3) 
Read Chapter 4.24.4: Linear Classificaton Methods for Binary Problems 
Lecture 4: Binary Classification (I): Basics 


Homework 2 Assignment. Assigned on Jan 29, due on Feb 12. 


Homework 2 Solution, Code 
Week 6 (Feb 4  Feb 10) 
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 (Feb 11  Feb 17) 
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 Feb 12, due on Feb 26 


Homework 3 Solution, Code 
Week 5 (Feb 18  Feb 24) 
Topic: Nonlinear Discriminant Analysis 
Lecture 9: QDA and RDA 

Supplementary Reading: LDA for improved large vocabulary continuous speech recognition 
Lecture 10: PCA 
Week 6 (Feb 25  March 3) 
Topic: Linear Regression Models 
Lecture 11: Linear Regression 

Read Chapter 3 : Linear Regression, Supplementary Reading: Linear Model Theory 



Homework 4 Assignment. Assigned on March 5, due on March 26. 
Week 7 (March 11  March 17) 
Read Chapter 3 : Variable Selection for Linear Regression 
Lecture 12: Variable Selection (I) 

Reading: Regression Shrinkage and Selection via the LASSO, 

Week 8 (March 18  March 24) 

Lecture 13: Shrinkage Methdods by LASSO


Supplementary Reading: Regularization and variable selection via the
elastic net 

Week 9 (March 25  March 31) 

Final Project: Project assigned on March 26, due on May
12 

Final Project Suggested Reading List 
Homework 5, Prostate data set,
data info. Assigned on March 26th, due on April 9



Lecture 14: Beyond LASSO 
Week 10 (April 1  7) 

Lecture 15: Model Selection and Assessment 

Supplenmentary Reading: Leaveoutone Cross Validation 

Week 11 (April 8  14) 
Read Chapter 4 (4.5) 
Lecture 16: Modern Classification vis Separting Hyperplanes 

Read Chapter 12 
Lecture 17: Support Vector Machines 

Supplementary Reading: The Entire Regularization Path for the Support Vector Machine 
Lecture 18: Multiclass Support Vector Machines 

Read Chapter 12 
Lecture 19: Optimization Programming 
Week 12 (April 15  21) 
Read Chapter 9 (9.2) : Treebased Methods 
Lecture 20: Classification and Regression Trees 


Homework 6, assigned on April 16, due on April 30.

Read Chapter 8.7 : Bootstrap and Bagging 
Supplenmentary Reading: Explaining Adaboost 
Lecture 21: Bagging and Boost 
Week 13 (April 22  28) 
Read Chapter 14 (14.114.4) : Unsupervised Learning 
Lecture 22: Cluster Analysis 

Recommender Systems 

Week 14 (April 29 30) 
Graphical Models and Network Analysis 
