Syllabus/achievement requirements

Textbook: Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome: The Elements of Statistical Learning, 2009. 2nd edition, Springer.

Curriculum:

• Chapter 1: Introduction
  • plus Carmichael & Marron (2018)
• Chapter 2: Overview of Supervised Learning
• Chapter 3: Linear Methods for Regression
  • plus Zou (2006)
• Chapter 5: Basis Expansions and Regularization
• Chapter 6: Kernel Smoothing Methods
  • plus Hjort & Glad (1995)
• Chapter 7: Model Assessment and Selection
• Chapter 8: Model Inference and Averaging
• Chapter 9: Additive Models, Trees, and Related Methods
• Chapter 10: Boosting and Additive Trees
  • plus B¨¹hlmann & Yu (2003)
• Chapter 11: Neural Networks
  • plus Schmidhuber (2015)
• Chapter 15: Random Forests
• Chapter 16: Ensemble Learning
• Chapter 18: High-Dimensional Problems: p>>n
  • plus Meinshauser & B¨¹hlmann (2010)