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Syllabus of ARTIFICIAL INTELLIGENCE (NCS-702)
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.
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
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.
Machine Learning : Supervised and unsupervised learning, Decision trees, Statistical learning models,
Learning with complete data – Naive Bayes models, Learning with hidden data – EM algorithm,
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.
TOTAL LECTURE: 45
- Stuart Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, Pearson Education
- Elaine Rich and Kevin Knight, “Artificial Intelligence”, McGraw-Hill
- E Charniak and D McDermott, “Introduction to Artificial Intelligence”, Pearson Education
- Dan W. Patterson, “Artificial Intelligence and Expert Systems”, Prentice Hall of India,