Introduction to Artificial Intelligence-Search-Heuristic Search-A* algorithm-Game Playing- Alpha-Beta Pruning- Expert systems-Inference-Rules-Forward Chaining and Backward Chaining- Genetic Algorithms.
Proposition Logic - First Order Predicate Logic – Unification – Forward Chaining -Backward Chaining - Resolution – Knowledge Representation - Ontological Engineering - Categories and Objects – Events - Mental Events and Mental Objects - Reasoning Systems for Categories - Reasoning with Default Information - Prolog Programming.
Non monotonic reasoning-Fuzzy Logic-Fuzzy rules-fuzzy inference-Temporal Logic-Temporal Reasoning-Neural Networks-Neuro-fuzzy Inference.
Probability basics - Bayes Rule and its Applications - Bayesian Networks – Exact and Approximate Inference in Bayesian Networks - Hidden Markov Models - Forms of Learning - Supervised Learning - Learning Decision Trees – Regression and Classification with Linear Models - Artificial Neural Networks – Nonparametric Models - Support Vector Machines - Statistical Learning - Learning with Complete Data - Learning with Hidden Variables- The EM Algorithm – Reinforcement Learning
Natural language processing-Morphological Analysis-Syntax analysis-Semantic Analysis-AIl applications – Language Models - Information Retrieval – Information Extraction - Machine Translation – Machine Learning - Symbol-Based – Machine Learning: Connectionist – Machine Learning.
Reference Book:
1. Patrick H. Winston. "Artificial Intelligence", Third edition, Pearson Edition, 2006. 2. Dan W.Patterson, ―Introduction to Artificial Intelligence and Expert Systemsǁ, PHI, 2006. 3. Nils J. Nilsson, ―Artificial Intelligence: A new Synthesisǁ, Harcourt Asia Pvt. Ltd., 2000.
Text Book:
1. Stuart Russell, Peter Norvig, ―Artificial Intelligence: A Modern Approachǁ, Third Edition, Pearson Education / Prentice Hall of India, 2010. 2. Elaine Rich and Kevin Knight, ―Artificial Intelligenceǁ, Third Edition, Tata McGraw-Hill, 2010.