## 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
- Kalman & particle filters

- Fast N-body learning
- KD trees
- Dual trees
- Distance transform
- Fast multipole methods

- Kernel methods:
- Kernel ridge regression
- Kernel lasso
- Gaussian processes for prediction
- Semi-supervised learning with kernels
- RKHS
- Support vector machines
- Kernel PCA
- Kernels on structured data
- Learning with Gaussian processes
- MCMC
- Variational methods
- Laplace approximation

- Latent processes
- Relevance vector machines
- Online classification with particle filters

- Active learning
- Bayesian and minimax decision theory
- Active learning with Gaussian processes

- Exponential families
- Sufficient statistics
- Maximum likelihood, KL and maximum entropy
- Generalized linear models
- Exponential families on graphs
- Exponential families and AI