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Date Summary

06/01/2014

Overview of the course

09/01/2014--20/01/2014

Representation: graphical models (Chapter 3 of KF)

  • Directed graphical models: overview, representation of probability distribution and conditional independence statements.

 

16/01/2014

Quiz Syllabus: Chapter 2 and material covered up to 13/01/2014.

23/01/2014--30/01/2014

Undirected graphical models (Chapter 5 of KF)

  • Representation: potentials, conditional independence and graph separability, factorization.
  • Constructing undirected models from distributions
  • Relationship between directed and undirected models.
  • Common undirected graphical models: Factor models, Ising and Potts model, Gibbs distribution, log-linear models, CRFs.
  • Feature-based potentials for flexible deployment in many applications.
  • Application in vision and text mining.

 

03/02/2014 --- 13/02/2014

Exact Inference

  • Overview (Chapter 9 of KF)
  • Variable elimination (Chapter 9 of KF)
  • Junction trees and sum product message passing (Chapters 10 of KF)
  • Creating junction trees, Junction trees: triangulation and decomposability (Chapters 10 of KF)

 

Midsems week

24/02/2014 --- 27/02/2014

Combinatorial algorithms for MAP inference. (Chapter 13 in KF)

  • Fast Approximate Energy Minimization via Graph Cuts, Yuri Boykov, Olga Veksler and Ramin Zabih, IEEE Transactions on PAMI, vol. 23, no. 11, pp. 1222-1239. local copy
  • What energy functions can be minimized via graph cuts. local copy

 

03/03/2014--10/03/2014

Inference via sampling. (Chapter 12 in KF)

  • Forward sampling, rejection sampling, and likelihood weighted sampling in Bayesian networks
  • Importance sampling and relationship to likelihood weighted sampling
  • MCMC and Gibbs sampling in general graphical models
13/03/2014---27/04/2014

Learning graphical model parameters (probabilistic methods)

  • Parameter estimation in Bayesian networks (completely observed variable set): MLE (Chapter 16, 17.1--17.4.3 )
  • Local CPDs: exponential familiy (Chapter 8 from KF)
  • Parameter estimation in undirected graphs (Chapter 20.1--20.2.3)
  • Learning conditional graphical models (CRFs), conditional likelihood training. (Chapter 20.3.1--20.3.2)
  • Learning with partially observed data, training a HMM
  •  

27/03/2014--03/04/2014

Structured learning.

 

07/04/2014---10/04/2014

Learning graphical model structure

 

14/04/2014 -- 14/04/2014

Approximate inference based on loopy belief propagation

  • Chapter 11.3.1--11.3.5 of KF