CS6730 - Probablistic Reasoning in AI

Course Data :

  • Introduction: Basic probability notations, Axioms of probability, Probability distributions, Bayes theorem.
  • Bayesian Networks: Semantics of Bayesian networks, Exact inferencing - enumeration, variable elimination, junction-tree Approximate inferencing - sampling methods, Markov chain Monte Carlo methods, variational methods, particle filtering.
  • Reasoning over time: inference in temporal models, hidden markov models, Kalman filters, dynamic Bayesian networks, efficient representations of CPTs.
  • Decision making under uncertainity: Beliefs and utility, Utility theory, utility functions, risk modeling, decision networks, Value of Information.
  • Decision theoretic planning: Sequential decision making, Markov decision processes. Value iteration, policy iteration, Real-time dynamic programming, multi-agent planning: game theory, reinforcement learning, rolls outs.

Pre-Requisites

    None

Parameters

Credits Type Date of Introduction
4-0-0-0-8-12 Elective Jan 2008

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