CS229 notebook

Supervised learning. (7 classes)

  • Supervised learning setup. LMS.
  • Logistic regression. Perceptron. Exponential family.
  • Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes.
  • Support vector machines.
  • Model selection and feature selection.
  • Ensemble methods: Bagging, boosting.
  • Evaluating and debugging learning algorithms.

Learning theory. (3 classes)

  • Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds.
  • VC dimension. Worst case (online) learning.
  • Practical advice on how to use learning algorithms.

Unsupervised learning. (5 classes)

  • Clustering. K-means.
  • EM. Mixture of Gaussians.
  • Factor analysis.
  • PCA (Principal components analysis).
  • ICA (Independent components analysis).

Reinforcement learning and control. (4 classes)

  • MDPs. Bellman equations.
  • Value iteration and policy iteration.
  • Linear quadratic regulation (LQR). LQG.
  • Q-learning. Value function approximation.
  • Policy search. Reinforce. POMDPs.