Topics will include: 

  • Review of probability theory and basic calculus
  • Graphical model frameworks: Bayes networks, Markov networks
  • Exact inference: Variable elimination, conditioning, clique trees
  • Approximate inference: Belief propagation, graph cuts, particle-based inference
  • Inference as optimization
  • Optimization techniques: Gradient descent, Newton methods, constrained optimization, stochastic optimization, genetic algorithsm- Learning: maximum likelihood and MAP parameter estimation, structure learning, Expectation-Maximization
  • Temporal models: Markov chains, hidden Markov models
  • Applications