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)

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