05-01-2017, 07:43 PM
SYLLABUS:-
UNIT-I
Bayesian network, Examples (HMM, diagnostic system, etc.), Separation and independence, Markov properties and minimalism, Markov network, Examples (Boltzmann machine, Markov random field, etc.), Cliques and potentials, Markov properties
UNIT-II
Exact inference, Complexity, Bucket elimination, Junction tree, Belief propagation (message passing), Application to HMM, Sum- and Max-product algorithms.
UNIT-III
Parameter learning, Exponential family, Bayesian learning, Expectation-Maximization (EM)
UNIT-IV
Approximate inference, Convexity, Mean field approach, Structured variational method, Loopy belief propagation, Characterization of solution spaces, Sampling methods.
UNIT-I
Bayesian network, Examples (HMM, diagnostic system, etc.), Separation and independence, Markov properties and minimalism, Markov network, Examples (Boltzmann machine, Markov random field, etc.), Cliques and potentials, Markov properties
UNIT-II
Exact inference, Complexity, Bucket elimination, Junction tree, Belief propagation (message passing), Application to HMM, Sum- and Max-product algorithms.
UNIT-III
Parameter learning, Exponential family, Bayesian learning, Expectation-Maximization (EM)
UNIT-IV
Approximate inference, Convexity, Mean field approach, Structured variational method, Loopy belief propagation, Characterization of solution spaces, Sampling methods.