Introduction to Data Mining
Lecture 2 (1/15): data summaries
Lecture 3 (1/17): data visualization
Supervised methods Lecture 4 (1/22): intro to regression and linear regression
Lecture 5 (1/24, 1/29): categorical predictors and interactions
Lecture 6 (1/29, 2/5): K nearest neighbors (1/31 university closed for weather)
Lecture 7 (2/5): Model assessment and the bias/variance trade-off
Lecture 8 (2/7, 2/12): Linear and Quadratic Discriminant Analysis
Lecture 9 (2/12, 2/14): Logistic regression
Lecture 10 (2/19): Resampling methods (cross-validation and bootstrap)
Lecture 11: Model Selection
Part 1 (2/21): best subset selection
Part 2 (2/26): shrinkage methods
Part 3 (2/28): dimension reduction
Lecture 12: nonlinear regression
Part 1 (3/19): Splines
Part 2 (3/21): Generalized Additive Models (GAMs)
Upcoming lecture topics (subject to change)
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Trees
Ensemble methods
Support Vector Machines
Unsupervised Methods
Introduction to Clustering
Hierarchical clustering
K-means