Artificial Intelligence
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Artificial Intelligence Syllabus — Unit-wise Content
The official GTU curriculum for Artificial Intelligence (subject code 3170716) as prescribed for Computer Engineering · Semester 7 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 (3170716)Unit 1 Introduction
- 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
- 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
- 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
- Introduction To Nonmonotonic Reasoning
- Logics For Non-monotonic Reasoning
Unit 5 Probabilistic Reasoning
- Probability And Bays’ Theorem
- Certainty Factors And Rule- Base Systems
- Bayesian Networks
- Dempster-Shafer Theory
- Fuzzy Logic
Unit 6 Game Playing
- MiniMax Search Procedure
- Alpha-Beta Cut-offs
- Iterative deepening
Unit 7 Planning
- 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
- Syntactic Processing
- Semantic Analysis
- Discourse And Pragmatic Processing
- Spell Checking 3 Bachelor of Computer Engineering Subject Code: 3170716 w.e.f
Unit 9 Connectionist Models
- Introduction: Hopfield Network
- Learning In Neural Network
- Application Of Neural Networks
- Recurrent Networks
- Distributed Representations
- Connectionist AI And Symbolic AI
Unit 10 Expert Systems
- Representing and Using Domain Knowledge
- Expert System Shells
- Knowledge Acquisition
Unit 11 Genetic Algorithms
- A Peek into the Biological World
- Genetic Algorithms (GAs)
- Significance of the Genetic Operators
- Termination Parameters
Unit 12 Introduction to Prolog
- Converting English to Prolog Facts and Rules
- Prolog Terminology
- Control Structures
- Arithmetic Operators
- Matching in Prolog
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