UNIT 1:
Introduction to Artificial Intelligence
Search- Breadth First, Uniform Cost, Depth first
Search- Breadth First, Uniform Cost, Depth first
Search- Breadth First, Uniform Cost, Depth first
Search- Depth limited, Iterative deepening depth-first, Bidirectional Search
Heuristic Search-A* algorithm
Game Playing- Alpha-Beta Pruning
Game Playing- Alpha-Beta Pruning
Forward Chaining and Backward Chaining
UNIT 2:
Proposition Logic - First Order Predicate Logic
Proposition Logic - First Order Predicate Logic
Proposition Logic - First Order Predicate Logic
Unification – Forward Chaining
Unification – Forward Chaining
Backward Chaining - Resolution
Knowledge Representation - Ontological Engineering
Categories and Objects – Events
Mental Events and Mental Objects
Reasoning Systems for Categories
Reasoning with Default Information
UNIT 3:
UNIT 4:
Probability basics - Bayes Rule and its Applications
Probability basics - Bayes Rule and its Applications
Bayesian Networks – Exact and Approximate Inference in Bayesian Networks
Bayesian Networks – Exact and Approximate Inference in Bayesian Networks
Bayesian Networks – Exact and Approximate Inference in Bayesian Networks
Hidden Markov Models - Forms of Learning
Hidden Markov Models - Forms of Learning
Supervised Learning - Learning Decision Trees
Artificial Neural Networks – Nonparametric Models
Support Vector Machines - Statistical Learning
Support Vector Machines - Statistical Learning
UNIT 5:
Syntax analysis-Semantic Analysis
Natural language processing
Syntax analysis-Semantic Analysis
AIl applications – Language Models
AIl applications – Language Models
Machine Learning - Symbol-Based
