Principles of search, uninformed search, informed (heuristic) search, genetic algorithms, game playing – Basic idea behind search algorithms. Complexity. Combinatorial explosion and NP completeness. Polynomial hierarchy. Uninformed Search – Depth-first. Breadth-first. Uniform-cost. Depth-limited. Iterative deepening. Informed search – Best-first. A* search. Heuristics. Hill climbing. Problem of local extrema. Simulated annealing. Genetic Algorithms.
Knowledge bases and inference; constraint satisfaction, logical reasoning – Fuzzy logic. Reasoning under uncertainty – probabilities, conditional independence, Markov blanket, Bayes Nets – Probabilistic inference, enumeration, variable elimination, approximate inference by stochastic simulation, Markov chain Monte Carlo, Gibbs sampling. Agents that reason logically – Knowledge-based agents. Logic and representation. Propositional (Boolean) logic, Inference in propositional logic. Syntax. Semantics. Probabilistic Reasoning over time: Temporal models, Hidden Markov Models, Kalman filters, Dynamic Bayesian Networks, Automata theory. Planning – Definition and goals. Basic representations for planning. Situation space and plan space.
Inductive learning, concept formation, decision tree learning, statistical approaches, probabilistic methods, learning from examples – neural networks – Probability-Based Learning: Probabilistic Models, Naïve Bayes Models, EM algorithm, Introductions to AI Ethics, Heterogeneous Data Acquisition techniques, Reinforcement Learning.