Biju Patnaik University of Technology, Odisha, Electronics and Communication engineering Semester 6, Artificial Intelligence and Machine Learning Syllabus

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Unit - 1 Introduction

Unit – 1

1.1 The Foundations of Artificial Intelligence

1.2 Intelligent Agents – Agents and Environments

1.3 Good Behaviour The Concept of Rationality the Nature of Environments the Structure of Agents

1.4 Solving Problems by Search – ProblemSolving Agents Formulating problems Searching for Solutions

1.5 Uninformed Search Strategies Breadthfirst search

1.6 Depthfirst search

1.7 Searching with Partial Information

1.8 Informed Heuristic Search Strategies

1.9 Greedy bestfirst search

1.10 A* Search CSP

1.11 MeansEndAnalysis

Unit - 2 ADVERSARIAL SEARCH

Unit – 2

2.1 Adversarial Search – Games

2.2 The MiniMax algorithm optimal decisions in multiplayer games

2.3 AlphaBeta Pruning Evaluation functions Cutting off search

2.4 Logical Agents – KnowledgeBased agents

2.5 Logic

2.6 Propositional Logic Reasoning Patterns in Propositional Logic Resolution

2.7 Forward and Backward chaining FIRST ORDER LOGIC – Syntax and Semantics of FirstOrder Logic Using FirstOrder Logic

2.8 Knowledge Engineering in FirstOrder Logic Inference in First Order Logic – Propositional vs. FirstOrder Inference

2.9 Unification and Lifting

2.10 Forward Chaining Backward Chaining

2.11 Resolution

Unit - 3 UNCERTAINTY

Unit – 3

3.1 Uncertainty – Acting under Uncertainty

3.2 Basic Probability Notation

3.3 The Axioms of Probability

3.4 Inference Using Full Joint Distributions Independence

3.5 Bayes’ Rule and its Use

3.6 Probabilistic Reasoning – Representing Knowledge in an Uncertain Domain

3.7 The Semantics of Bayesian Networks

3.8 Efficient Representation of Conditional Distribution

3.9 Exact Inference in Bayesian Networks

3.10 Approximate Inference in Bayesian Networks

Unit - 4 LEARNING METHODS

Unit – 4

4.1 Statistical Learning Learning with Complete Data Learning with Hidden Variables Rote Learning Learning by Taking Advice

4.2 Learning in Problemsolving learning from Examples Induction

4.3 Explanationbased Learning Discovery Analogy Formal Learning Theory

4.4 Neural Net Learning and Genetic Learning

4.5 Expert Systems Representing and Using Domain Knowledge Expert System Shells Explanation

4.6 Knowledge Acquisition

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