“It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers… They would be able to converse with each other to sharpen their wits. At some stage, therefore, we should have to expect the machines to take control.” - Alan Turing
Course Code: CSE551
Credits: 3:0:0:0
Contact Hours: 42
Prerequisites: Knowledge of any advanced programming language, Algorithms and Data structures, Elementary Discrete Mathematics or similar.
Course Coordinators: Dr. Annapurna P Patil and Mr. Shreekant Jere
Course Contents:
Unit I
Introduction: What is AI? Foundation and History of Artificial Intelligence. Intelligent Agents: Agents and Environments, Rationality, The Nature of Environments, The Structure of Agents. Problem-solving by search: Problem-Solving Agents, Example Problems, Searching for Solution, Uniformed Search Strategies, Informed Search Strategies, Heuristic Functions. (Chapter 1, 2, 3 of Text Book 1)
Unit II
Logical Agents: Knowledge-Based Agents, The Wumpus World, Logic, Propositional Logic, Reasoning patterns in propositional Logic, Effective Propositional Model Checking, Agents Based on Propositional Logic. First-Order Logic: Representation Revisited, Syntax and Semantics of First-Order Logic, Using First-Order Logic, Knowledge Engineering in First-Order Logic. Interference in First-order Logic: Propositional vs. First-Order Inference, Unification and Lifting, Forward chaining, Backward chaining, Resolution. (Chapter 7, 8, 9 of Text Book 1)
Unit III
Planning: Definition, Planning with State-Space Search, Planning Graphs, Other Planning Approaches Analysis. Uncertainty: Acting under Uncertainty, Basic Probability Notations, Inference using Full Joint Distributions, Independence, Bayes’ Rule and its Use. Learning from Examples: Forms of Learning, Supervised Learning, Learning Decision Trees, Artificial Neural Networks, Support Vector Machines, Ensemble Learning. (Chapter 10, Chapter 13, Chapter 18.1,18.2,18.3, 18.7,18.9,18.10 of Text Book 1)
Unit IV
Natural Language Processing: Language Models, Text Classification, Information Retrieval, Information Extraction. Natural Language communication: Phrase Structure Grammars, Syntactic Analysis, Augmented Grammars and Semantic Interpretation, Machine translation, Speech recognition. (Chapter 22, 23 of Text Book 1)
Unit V
Genetic Algorithms: Genetic Algorithms Introduction, Significance of Genetic Operators, Termination Parameters, Niching and Speciation, Evolving Neural Networks, Theoretical Grounding, Ant Algorithms. Robotics: Introduction, Hardware, Perception, Planning to Move, Planning Uncertain Movement, Moving, Robotic Software Architecture, Application Domains. Philosophical Foundations: Weak and Strong AI, The Ethics and Risks of Developing AI, AI: The present and Future. (Chapter 23 of Text Book 2, Chapter 25, 26 ,27 of Text Book 1)
Text Books:
Stuart Russel, Peter Norvig: Artificial Intelligence - A Modern Approach, 3rd Edition, Pearson Education, 2012. (Unit-1,2,3,4,5).
Elaine Rich, Kevin Knight, Shivashankar B Nair: Artificial Intelligence, 3rd Edition, Tata McGraw Hill, 2011. (Unit-5).
Reference Books:
Peter Jackson, “Introduction to Expert Systems”, 3rd Edition, Pearson Education, 2007.(Unit 5).
Deepak Khemani “Artificial Intelligence”, Tata Mc Graw Hill Education 2013. (unit 3).
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