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Unit-I 10
Introduction : Introduction to Artificial Intelligence, Foundations and History of Artificial Intelligence,
Applications of Artificial Intelligence, Intelligent Agents, Structure of Intelligent Agents. Computer
vision, Natural Language Possessing.
Unit-II 10
Introduction to Search : Searching for solutions, Uniformed search strategies, Informed search strategies,
Local search algorithms and optimistic problems, Adversarial Search, Search for games, Alpha – Beta
Unit-III 10
Knowledge Representation & Reasoning: Propositional logic, Theory of first order logic, Inference in
First order logic, Forward & Backward chaining, Resolution, Probabilistic reasoning, Utility theory,
Hidden Markov Models (HMM), Bayesian Networks.
Unit-IV 10
Machine Learning : Supervised and unsupervised learning, Decision trees, Statistical learning models,
Learning with complete data – Naive Bayes models, Learning with hidden data – EM algorithm,
Reinforcement learning,
Unit-V 5
Pattern Recognition : Introduction, Design principles of pattern recognition system, Statistical Pattern
recognition, Parameter estimation methods – Principle Component Analysis (PCA) and Linear
Discriminant Analysis (LDA), Classification Techniques – Nearest Neighbor (NN) Rule, Bayes
Classifier, Support Vector Machine (SVM), K – means clustering.


  1. Stuart Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, Pearson Education
  2. Elaine Rich and Kevin Knight, “Artificial Intelligence”, McGraw-Hill
  3. E Charniak and D McDermott, “Introduction to Artificial Intelligence”, Pearson Education
  4. Dan W. Patterson, “Artificial Intelligence and Expert Systems”, Prentice Hall of India,
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