lecture notes

These notes are meant to be as brief (and concise) as possible. They are not full tutorials or lecture scripts.
Bayesian Logistic Regression (aka. Gaussian Process classification).
Describes the Bayesian version of logistic regression. Kernelization leads to equivalence to Gaussian Process classification; but also multi-class can naturally be handled.
Some notes on gradient descent.
(Gradient descent, monotonicity & stepsize adaptation, covariant & natural gradient, co- and contra-variance, relation to Newton step, Rprop)
Factor graphs and belief propagation.
(Graphical models, probabilistic inference, message passing algorithms, loopy BP)
Gaussian identities.
(Normal and canonical representation, product of Gaussians, linear transformation, marginals & conditionals, entropy, Kullback-Leibler divergence, mixture of Gaussians, collapsing)
Basic 3D geometry (for robotics).
(Rotation representations, transformations (static, dynamic, affine, contra-/co-variant), kinematic chains, Jacobian & Hessian)
Markov Decision Processes.
(definition, Bellman optimality equation, Q-function, computing value functions, value iteration, direct solution, policy iteration, Q-learning, TD(lambda), eligibility traces)
Influence Diagrams.
(brief definition, inference methods in influence diagrams, relation to MDPs)
Stochastic Optimal Control.
(discrete time formulation, linear-quadratic-Gaussian case, Riccati equations, message passing formulation, classical cost formulation)