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 multiclass can naturally be handled.
 Some notes on gradient descent.
 (Gradient descent, monotonicity & stepsize adaptation, covariant & natural gradient, co and contravariance, 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, KullbackLeibler divergence, mixture of
Gaussians, collapsing)
 Basic 3D geometry (for robotics).

(Rotation representations, transformations (static, dynamic, affine,
contra/covariant), kinematic chains, Jacobian & Hessian)
 Markov Decision Processes.

(definition, Bellman optimality equation, Qfunction, computing value
functions, value iteration, direct solution, policy iteration,
Qlearning, TD(lambda), eligibility traces)
 Influence Diagrams.
 (brief
definition, inference methods in influence diagrams, relation to MDPs)
 Stochastic Optimal Control.
 (discrete time
formulation, linearquadraticGaussian case, Riccati equations,
message passing formulation, classical cost formulation)
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