Machine Learning Contents

  • Introduction to machine learning
  • Spectral methods:
    • Eigenvalues and the SVD.
    • Google
    • Text retrieval
    • Image compression
    • Data visualization and PCA
  • Linear models:
    • Least squares
    • Ridge regression & cross-validation
    • Constrained optimization, lasso and feature selection
    • Maximum likelihood and Bayesian learning
    • Conjugate analysis
    • Introduction to MCMC
  • Kernel methods:
    • Kernel ridge regression
    • Support vector machines
    • Kernel dimensionality reduction
    • Semi-supervised learning with kernels on graphs
  • Learning with Gaussian processes
    • Nonlinear regression
    • Variational methods
    • Laplace approximation
    • Expectation propagation
  • Active learning
    • Bayesian and minimax decision theory
    • Active learning with Gaussian processes
    • Bandit problems
  • Dynamic models
    • Hidden Markov models
    • Kalman filters
    • Particle filters
  • Partially observed Markov decision processes
    • Reinforcement learning
    • Direct policy search
  • Probabilistic graphical models and causality
  • Mixture models and the EM algorithm
  • Computational learning theory