UPTU Artificial Intelligence Syllabus

ECS-801: Artificial Intelligence

Unit-I

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

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 pruning.

Unit-III

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

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

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.

References:

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,