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

Class location/time: Remote via Zoom (T & Th 2:20PM - 3:40PM)

Office hours: Aaditya Ramdas: (T 4-5pm), Ian Waudby-Smith: (W 1-2pm)

• Overview
  
• Syllabus
  
  

• Homework 1
• Homework 2
• Homework 3
• Homework 4

• Crowd-scribing sign-up sheet (access with a CMU account)
  
• Crowd-scribed class 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 from Spring 2015 course, by Nicole Rafidi
  
• 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