36-708: The ABCDE of Statistical Methods in Machine Learning
Spring 2022

Class location/time: DH 1211 (T & Th 1:25PM - 2:45PM)

Office hours: Aaditya Ramdas: (T 3-4pm), Ian Waudby-Smith: (W 3-4pm)

• Overview
  
• Syllabus
  
  

• HW1
• HW2
• HW3
• HW4

• Sign-up sheet (requires you to be signed into your CMU gmail account)
  
• Scribed notes (read-only; ask for edit link if in class)

• The Elements of Statistical Learning: Data Mining, Inference and Prediction. Trevor Hastie, Robert Tibshirani, Jerome Friedman.
  
• An Introduction to Statistical Learning: With Applications in R. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
  
• Foundations of Machine Learning. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.
  
  

• Videos of Larry Wasserman's 36-705 course
  
• Linear algebra review, videos by Zico Kolter
  
• Real analysis, calculus, and more linear algebra, videos by Aaditya Ramdas
  
• Convex optimization prequisites review
  
• See also Appendix A of Boyd and Vandenberghe (2004) for general mathematical review
  
  

• A list of machine learning books
  
• A course in machine learning
  
• Interpretable Machine Learning
  
• Fairness and machine learning