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Rashtrasant Tukadoji Maharaj Nagpur University, Maharashtra, Computer Engineering Semester 6, Artificial Intelligence Syllabus
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Unit - 1 Introduction
Unit 1
Introduction
1.1 What is AI
1.2 History Applications
1.3 Artificial intelligence as representation Search
1.4 Production system
1.5 Basics of problem solving problem representation paradigms
1.6 Defining problem as a state space representation
1.7 Characteristics
Unit 1
Introduction
1.1 What is AI
1.2 History Applications
1.3 Artificial intelligence as representation Search
1.4 Production system
1.5 Basics of problem solving problem representation paradigms
1.6 Defining problem as a state space representation
1.7 Characteristics
Unit - 2 Search Techniques
Unit 2
Search Techniques
2.1 Uninformed Search techniques
2.2 Informed Heuristic Based Search
2.3 Generate and test
2.4 Hillclimbing
2.5 BestFirst Search
2.6 Problem Reduction and Constraint Satisfaction
Unit 2
Search Techniques
2.1 Uninformed Search techniques
2.2 Informed Heuristic Based Search
2.3 Generate and test
2.4 Hillclimbing
2.5 BestFirst Search
2.6 Problem Reduction and Constraint Satisfaction
Unit - 3 Knowledge representation
Unit 3
Knowledge representation
3.1 Knowledge representation
3.2 Issues First order logic
3.3 Predicate Logic
3.4 Structured Knowledge Representation Backward Chaining
3.5 Resolution
3.6 Semantic Nets
3.7 Frames and Scripts Ontology
Unit 3
Knowledge representation
3.1 Knowledge representation
3.2 Issues First order logic
3.3 Predicate Logic
3.4 Structured Knowledge Representation Backward Chaining
3.5 Resolution
3.6 Semantic Nets
3.7 Frames and Scripts Ontology
Unit - 4 Uncertainty
Unit 4
Uncertainty
4.1 Handing uncertain knowledge
4.2 Rational decisions
4.3 Basics of probability
4.4 Axioms of probability
4.5 Bayes Rule and conditional independence
4.6 Bayesian networks
4.7 Exact and Approximate inference in Bayesian Networks
4.8 Fuzzy Logic
Unit 4
Uncertainty
4.1 Handing uncertain knowledge
4.2 Rational decisions
4.3 Basics of probability
4.4 Axioms of probability
4.5 Bayes Rule and conditional independence
4.6 Bayesian networks
4.7 Exact and Approximate inference in Bayesian Networks
4.8 Fuzzy Logic
Unit - 5 Learning
Unit 5
Learning
5.1 What is learning
5.2 Knowledge and learning
5.3 Learning in Problem Solving
5.4 Learning from example
5.5 Learning probabilistic models
5.6 Formal Learning Theory
Unit 5
Learning
5.1 What is learning
5.2 Knowledge and learning
5.3 Learning in Problem Solving
5.4 Learning from example
5.5 Learning probabilistic models
5.6 Formal Learning Theory
Unit - 6 Expert Systems
Unit 6
Expert Systems
6.1 Expert Systems Fundamental blocks
6.2 Knowledge Engineering
6.3 Knowledge Acquisition
6.4 Knowledge Based Systems
6.5 Automated Reasoning
6.6 Understanding Natural language
Unit 6
Expert Systems
6.1 Expert Systems Fundamental blocks
6.2 Knowledge Engineering
6.3 Knowledge Acquisition
6.4 Knowledge Based Systems
6.5 Automated Reasoning
6.6 Understanding Natural language
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