UNIT 1:
AI Problems, AI Techniques, The Level of the Model
Heuristic Search Techniques: Generate-And- Test,
Constraint Satisfaction, Means-Ends Analysis
Constraint Satisfaction, Means-Ends Analysis
UNIT 2:
Forward Vs Backward Reasoning
Matching Techniques, Partial Matching,
Matching Techniques, Partial Matching,
Forward Vs Backward Reasoning
Logic Based Programming- AI Programming languages
Overview of LISP, Search Strategies in LISP,
Overview of LISP, Search Strategies in LISP,
Approaches to Knowledge Representation,
Procedural Vs Declarative Knowledge, Representations
Pattern matching in LISP ,
Over view of Prolog, Production System using Prolog.
UNIT 3:
First Order Predicate Logic: Representing Instance and is-a relationship
First Order Predicate Logic: Representing Instance and is-a relationship
Syntax & Semantics of FOPL,
Computable Functions and Predicates,
Normal Forms, Unification &Resolution, Representation Using Rules
Normal Forms, Unification &Resolution, Representation Using Rules
Structured Representations of Knowledge: Semantic Nets
Partitioned Semantic Nets,
Conceptual Graphs, Scripts, CYC
UNIT 4:
Learning by Advise, Examples, Learning in problem Solving
Symbol Based Learning, Explanation Based Learning
Unsupervised Learning, Reinforcement Learning
Symbol Based Learning, Explanation Based Learning
supervised Learning: Perceptron Learning
supervised Learning: Perceptron Learning
Back propagation Learning
UNIT 5:
TF-IDF, Named Entity Recognition,
Role of Knowledge in Language Understanding
Approaches Natural Language Understanding
Steps in The Natural Language Processing
Syntactic Processing and Augmented Transition Nets,
POS Tagging, Tokenization
Steps in The Natural Language Processing
