Artificial Intelligence

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ARTIFICIAL INTELLIGENCE 3164802
FREE PDF 3164802
Code: 3164802 Credits: 4 Semester 6 CS-Cyber

Artificial Intelligence

Get the full GTU study material, English & Gujarati textbook PDFs, syllabus curriculum, and solved previous year question papers.

Artificial Intelligence Syllabus — Unit-wise Content

The official GTU curriculum for Artificial Intelligence (subject code 3164802) as prescribed for CS-Cyber · Semester 6 divides the course into 12 units, covering 60 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.

Download the official syllabus PDF (3164802)

Unit 1 Introduction

3 lecture hours

  • The AI Problems
  • The Underlying Assumption
  • AI techniques
  • The Level of The Model
  • Criteria for Success

Unit 2 Problems, State Space Search & Heuristic Search Techniques

6 lecture hours

  • Defining The Problems As A State Space Search
  • Production Systems
  • Production Characteristics
  • Production System Characteristics and Issues in the Design of Search Programs
  • Hill Climbing
  • Best-First Search
  • Problem Reduction
  • Constraint Satisfaction
  • Means-Ends Analysis

Unit 3 Knowledge Representation

8 lecture hours

  • Representations And Mappings
  • Approaches To Knowledge Representation
  • Representation Simple Facts In Logic
  • Representing Instance And Isa Relationships
  • Computable Functions and Predicates
  • Procedural versus Declarative Knowledge
  • Logic Programming
  • Forward versus Backward Reasoning

Unit 4 Symbolic Reasoning Under Uncertainty

2 lecture hours

  • Introduction To Nonmonotonic Reasoning
  • Logics For Non-monotonic Reasoning

Unit 5 Probabilistic Reasoning

3 lecture hours

  • Probability And Bays’ Theorem
  • Certainty Factors And Rule Base Systems
  • Bayesian Networks
  • Dempster-Shafer Theory
  • Fuzzy Logic

Unit 6 Game Playing

5 lecture hours

  • MiniMax Search Procedure
  • Alpha-Beta Cut-offs
  • Iterative deepening

Unit 7 Planning

4 lecture hours

  • The Blocks World
  • Components Of a Planning System
  • Goal Stack Planning
  • Nonlinear Planning Using Constraint Posting
  • Hierarchical Planning
  • Reactive Systems

Unit 8 Natural Language Processing

4 lecture hours

  • Syntactic Processing
  • Semantic Analysis
  • Discourse And Pragmatic Processing
  • Spell Checking

Unit 9 Connectionist Models

4 lecture hours

  • Introduction: Hopfield Network
  • Learning In Neural Network
  • Application Of Neural Networks
  • Recurrent Networks
  • Distributed Representations
  • Connectionist AI And Symbolic AI

Unit 10 Expert Systems

2 lecture hours

  • Representing and Using Domain Knowledge
  • Expert System Shells
  • Knowledge Acquisition

Unit 11 Genetic Algorithms

4 lecture hours

  • A Peek into the Biological World
  • Genetic Algorithms (GAs)
  • Significance of the Genetic Operators
  • Termination Parameters

Unit 12 Introduction to Prolog

4 lecture hours

  • Converting English to Prolog Facts and Rules
  • Prolog Terminology
  • Control Structures
  • Arithmetic Operators
  • Matching in Prolog

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