Machine Learning

by Jorge Velez

Understanding Machine Learning: From Theory to Algorithms

• Entire Book (2014, ~500 pages):
• Chapters
  1. Introduction
  2. A Gentle Start: PAC Learning
  3. The Uniform Convergence Principle
  4. Support Vector Machines
  5. Kernel Methods
  6. Nearest Neighbor
  7. Decision Trees
  8. Boosting
  9. Model Selection
  10. Unsupervised Learning
  11. Foundations of Learning Theory
  12. Agnostic Learning
  13. Stability and Generalization
  14. Online Learning
  15. Learning with Experts and Bandits
  16. Reinforcement Learning
• Back Matter: References, Index

We humans are not the end of evolution, so if we can make a machine that's as smart as a person,
we can probably also make one that's much smarter.
There's no point in making just another person.
You want to make one that can do things we can't.

—Marvin Minsky