A 
Probabilistic reasoning (FC)
 Slides (✎ OCT 26, ✎ OCT 31, ✎ NOV 04 and ✎ NOV 07) Last update NOV 07
 Exercises (Last updated NOV 16  ✎ NOV 21 and ✎ NOV 23, with JC and N in LEC I)
Handin by NOV 27 at 23:59:59 Fortaleza time
 Results in [0,10] (Scores are PRELIMINARY: Submissions will be subjected to a 2nd round of evaluation)

 Probability refresher (Definitions, rules, tables, conditional probability)
 Probabilistic reasoning
 Prior, likelihood and posterior

B 
Graph concepts (FC)
 Slides (✎ NOV 09) Last update NOV 09

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

C 
Belief networks (FC)
 Slides (✎ NOV 11, ✎ NOV 14 and ✎ NOV 16)
 Exercises (✎ NOV 28 and ✎ NOV 30, with JC and N in LEC I, and ✎ DEC 02 with FC)
Handin by DEC 11 (was DEC 04) at 23:59:59 Fortaleza time
Results in [0,10].

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

D 
Graphical models (FC)

 Graphical models
 Markov networks (Markov properties, Markov random fields, HammersleyClifford theorem, Conditional independence using Markov networks, lattice models)
 Chain graphical models
 Factor graphs (Conditional independence)
 Expressiveness of graphical models

E 
Inference in trees (FC) 
 Marginal inference (Variable elimination in a Markov chain and message passing, the sumproduct algorithm of factor graphs, dealing with evidence, computing the marginal likelihood, loops)
 Forms of inference (maxproduct, finding the $N$ most probable states, the most probable path and the shortes path, mixed inference)
 Inference in multiply connected graphs (Bucket elimination, Loopcut conditioning)

F 
The Junction tree algorithm (FC) 
 Clustering variables (reparameterisation)
 Clique graphs (Absorption, absorption schedule on clique graphs)
 Junction trees (The running intersection property)
 Constructing a junction tree for singlyconnected distributions (moralisation, forming a clique graph, forming a junction tree from a clique graph, assigning potentials to cliques)
 Junction trees for multiply connected distributions (triangulation algorithm)
 The junction tree algorithm (remarks on the algorithm, computing the normalisation constant of a distribution, marginal likelihood, examples, ShaferShenoy propagation)
