A

Probabilistic reasoning (FC)

 Probability refresher
 Probabilistic reasoning, prior, likelihood and posterior

B 
Graph concepts (FC)

 Definitions, numerical encoding (edge lists, adjacency matrices, clique matrices)

C

Belief networks (FC)

 Structure (independencies and specifications)
 Belief networks (conditional independence, collisions, path manipulations for independence, dseparation, graphical and distributional in/dependence, Markov equivalence, expressibility)
 Causality (Simpson's paradox, docalculus, influence diagrams)

D

Inference in trees (FC)

 Marginal inference (Variable elimination in a Markov chain and message passing, the sumproduct algorithm of factor graphs, evidence, marginal likelihood, loops)
 Forms of inference (maxproduct, $N$ most probable states, most probable path and shortest path, mixed inference)
