Statistical Data Mining (IMEN 472)


Course Objectives

This course will cover theories and applications of basic (statistical) data mining techniques including regression, classification and unsupervised learning.


  • 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.


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