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Statistical Data Mining (IMEN 472)
Overview
Course Objectives
This course will cover theories and applications of basic (statistical) data mining techniques including regression, classification and unsupervised learning.
Textbook
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. (2nd edition). Springer.
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
Prerequisites
Linear algebra and basic statistics
Lecture Slides
Supplement 1: Linear Algebra [Slides]
Supplement 2: Multivariate Normal Distribution [Slides]
Supplement 3: Conditional Expectation [Slides]
Chapter 1: Introduction [Slides]
Chapter 2: Overview of Supervised Learning [Slides]
Chapter 3-1: Linear Methods for Regression (Part I) [Slides]
Chapter 3-2: Linear Methods for Regression (Part II) [Slides]
Chapter 4: Linear Methods for Classification [Slides]
Chapter 5: Nonparametric Methods for Regression and Classification [Slides]
Chapter 6: Decision Tree [Slides]
Chapter 7: Boosting [Slides]
Chapter 8: Random Forest [Slides]
Chapter 9: Support Vector Machine [Slides]
Chapter 10: Principal Component Analysis [Slides]
Chapter 11: Clustering Methods [Slides]
Lecture Videos
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