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Rashtrasant Tukadoji Maharaj Nagpur University, Maharashtra, Artificial Intelligence Semester 4, Introduction to 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
2.1 Uninformed Search techniques
Unit 1
Introduction
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
3.6 Semantic Nets
Unit 2
Search Techniques
3.1 Knowledge representation
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
4.1 Turing Machine Definition Model of TM
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 - 4 Uncertainty
Unit 4
Turing Machine
4.1 Turing Machine Definition Model of TM
4.2 Design of TM
4.3 Universal Turing Machine
4.4 Computable function
4.5 Recursive enumerable language
4.6 Types of TM’s
4.7 Linear Bounded Automata
4.8 Context sensitive language
Unit 4
Turing Machine
4.1 Turing Machine Definition Model of TM
4.2 Design of TM
4.3 Universal Turing Machine
4.4 Computable function
4.5 Recursive enumerable language
4.6 Types of TM’s
4.7 Linear Bounded Automata
4.8 Context sensitive language
Unit 4
Turing Machine
Unit 4
Turing Machine
4.1 Turing Machine Definition Model of TM
4.2 Design of TM
4.3 Universal Turing Machine
4.4 Computable function
4.5 Recursive enumerable language
4.6 Types of TM’s
4.7 Linear Bounded Automata
4.8 Context sensitive language
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
4.9 Intelligent Agents Introduction to Intelligent Agents
4.10 Rational Agent their structure
4.11 Reflex agent
4.12 Modelbased agent
4.13 Goalbased agent
4.14 Utilitybased agent
4.15 Behavior and environment in which a particular agent operates
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
4.9 Intelligent Agents Introduction to Intelligent Agents
4.10 Rational Agent their structure
4.11 Reflex agent
4.12 Modelbased agent
4.13 Goalbased agent
4.14 Utilitybased agent
4.15 Behavior and environment in which a particular agent operates
1.2 Use of feedback
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
4.9 Intelligent Agents Introduction to Intelligent Agents
4.10 Rational Agent their structure
4.11 Reflex agent
4.12 Modelbased agent
4.13 Goalbased agent
4.14 Utilitybased agent
4.15 Behavior and environment in which a particular agent operates
1.7 Block diagram
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
4.9 Intelligent Agents Introduction to Intelligent Agents
4.10 Rational Agent their structure
4.11 Reflex agent
4.12 Modelbased agent
4.13 Goalbased agent
4.14 Utilitybased agent
4.15 Behavior and environment in which a particular agent operates
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 Expert Systems Fundamental blocks
5.7 Knowledge Engineering
5.8 Knowledge Acquisition
5.9 Knowledge Based Systems
5.10 Basic understanding of Natural language
2.4 Eigen values and Eigen vectors Reduction to diagonal form
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 Expert Systems Fundamental blocks
5.7 Knowledge Engineering
5.8 Knowledge Acquisition
5.9 Knowledge Based Systems
5.10 Basic understanding of Natural language
2.1 Introduction and examples of vector spaces
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 Expert Systems Fundamental blocks
5.7 Knowledge Engineering
5.8 Knowledge Acquisition
5.9 Knowledge Based Systems
5.10 Basic understanding of Natural language
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