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