15.062 | Spring 2003 | Graduate

Data Mining


LEC # Topics Key Dates
1 Data Mining Overview, Prediction and Classification with k-Nearest Neighbors

2 Classification and Bayes Rule, Naïve Bayes

3 Classification Trees Homework 1 out
4 Discriminant Analysis

5 Logistic Regression Case: Handlooms

6 Neural Nets

7 Cases: Direct Marketing/German Credit Homework 1 due
Homework 2 out
8 Assessing Prediction Performance

9 Subset Selection in Regression

10 Regression Trees, Case: IBM/GM weekly returns Homework 2 due
11 k-Means Clustering, Hierarchical Clustering

12 Case: Retail Merchandising

13 Midterm Exam

Dimension Reduction
14 Principal Components

15 Guest Lecture by Dr. Ira Haimowitz: Data Mining and CRM at Pfizer

Data Base Methods
16 Association Rules (Market Basket Analysis)

17 Recommendation Systems: Collaborative Filtering

Wrap Up
18 Guest Lecture by Dr. John Elder IV, Elder Research: The Practice of Data Mining

19 Project Presentations

Course Info

As Taught In
Spring 2003
Learning Resource Types
assignment Problem Sets
grading Exams
notes Lecture Notes