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Syllabus

UNIT
1
Introduction: Artificial Intelligence

AI Problems, AI Techniques, The Level of the Model, Criteria or Success. Defining the Problem as a State Space Search, Problem Characteristics, Production Systems, Search: Issues in The Design of Search Programs, Un-Informed Search, BFS,DFS; Heuristic Search Techniques: Generate-And- Test, Hill Climbing, Best-First Search, A*Algorithm, Problem Reduction, AO*Algorithm, Constraint Satisfaction, Means-Ends Analysis

UNIT
2
Knowledge Representation

Procedural Vs Declarative Knowledge, Representations & Approaches to Knowledge Representation, Forward Vs Backward Reasoning, Matching Techniques, Partial Matching, Fuzzy Matching Algorithms and RETE Matching Algorithms; Logic Based Programming- AI Programming languages: Overview of LISP, Search Strategies in LISP,Pattern matching in LISP , An Expert system Shell in LISP, Over view of Prolog, Production System using Prolog.

UNIT
3
Symbolic Logic

Propositional Logic, First Order Predicate Logic: Representing Instance and is-a Relationships, Computable Functions and Predicates, Syntax & Semantics of FOPL, Normal Forms, Unification &Resolution, Representation Using Rules, Natural Deduction; Structured Representations of Knowledge: Semantic Nets, Partitioned Semantic Nets, Frames, Conceptual Dependency, Conceptual Graphs, Scripts, CYC.

UNIT
4
Machine Learning

Knowledge and Learning, Learning by Advise, Examples, Learning in problem Solving, Symbol Based Learning, Explanation Based Learning, Version Space, ID3 Decision Based Induction Algorithm, Unsupervised Learning, Reinforcement Learning, Supervised Learning: Perceptron Learning, Back propagation Learning, Competitive Learning, Hebbian Learning,Application-Speech Recognition-Self-driving cars-Virtual Personal Assistant

UNIT
5
Natural Language Processing

Role of Knowledge in Language Understanding, Approaches Natural Language Understanding, Steps in The Natural Language Processing, Syntactic Processing and Augmented Transition Nets, Semantic Analysis, NLP Understanding Systems; Statistical NLP ,Bag of Words Model, POS Tagging, Tokenization, Word Vectorizer, TF-IDF, Named Entity Recognition, Stop Words. Recommendation Systems,Application-Chatbots-Voice Assistants-Email Classification and Filtering

Reference Book:

1.Introduction to Artificial Intelligence & Expert Systems, Patterson, PHI 2.Patrick Henry Winston, Artificial Intelligence, 3rd Edition, AW, 1999 3.Introduction to Machine Learning - EthemAlpaydin, MIT Press, Prentice hall of India

Text Book:

Stuart Russell and Peter Norvig Artificial Intelligence - A Modern Approach, Prentice Hall, 3rd edition, 2011.

 

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