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
& crossvalidation
 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

Semisupervised 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