## Outline

**Active learning**
Expected utility and decision theory
Information theory
Linear experimental design
Gaussian processes and kernel methods
Active learning with GPs
Computational learning theory
Myopic vs long range active learning
Applications to graphics, attention, robotics, web crawling, bioinformatics, ...
**Reinforcement learning **
Stochastic dynamic programming and control
Markov decision processes
Bandit problems
Stochastic approximation and Q-learning
TD-lambda, sarsa, actor critic, value and policy iteration
POMDPS
Continuous problems
** Undirected graphical models **
Models
Inference algorithms
Parameter and structure learning algorithms