Topics

Textbook

Our primary source of readings will be A Course in Machine Learning (CML), a collection of notes by Hal Daumé III, which provides a gentle and thorough introduction to the field of machine learning.

Other recommended (but not required) books:

• Machine Learning: The Art and Science of Algorithms that Make Sense of Data by Peter Flach (ISBN 1107422221)
• Pattern Recognition and Machine Learning by Chris Bishop (ISBN 0387310738)
• Machine Learning by Tom Mitchell (ISBN 0070428077)
• Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman (ISBN 0387952845)
• Information Theory, Inference and Learning Algorithms by David MacKay (ISBN 0521642981)
• An Introduction to Computational Learning Theory by Michael Kearns and Umesh Vazirani (ISBN 0262111934)

Topics

• Foundations of Supervised Learning

  • Decision trees and inductive bias
  • Geometry and nearest neighbors
  • Perceptron
  • Practical concerns: feature design, evaluation, debugging
  • Beyond binary classification

• Advanced Supervised Learning

  • Linear models and gradient descent
  • Support Vector Machines
  • Naive Bayes models and probabilistic modeling
  • Neural networks
  • Kernels
  • Ensemble learning

• Unsupervised learning

  • K-means
  • PCA

• Selected advanced topics (as time permits)

  • Expectation maximization
  • Online learning
  • Markov decision processes
  • Imitation learning