Artificial Intelligence
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Follow on Instagram for UpdatesArtificial Intelligence Syllabus — Unit-wise Content
The official GTU curriculum for Artificial Intelligence (subject code 2720819) as prescribed for M( · Semester 2 divides the course into 5 units, covering 88 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 (2720819)Unit 1 Introduction
- Definition of AI, Emulation of human cognitive process
- knowledge search tradeoff
- stored knowledge
- semantic nets
- An abstract view of modelling
- elementary knowledge
- Computational logic
- analysis of compound statements using simple logic connectives
- predicate logic
- knowledge organization and manipulation
- knowledge acquisition
Unit 2 Programming
- Introduction to LISP
- syntax and numerical function
- LISP and PROLOG distinction
- input output and local variables
- Interaction and recursion
- property list and arrays alternative languages
- formalized symbolic logics- properties of WFRS
- non-deductive inference methods
- Inconsistencies and uncertainties: Truth maintenance systems
- default reasoning and closed world assumption
- Model and temporary logics
Unit 3 Search Methods and Knowledge Representation
- Fuzzy logic: Concepts
- Introduction to Fuzzy logic with examples
- probabilistic reasoning
- Bayesian probabilistic inference
- Dempstor Shafer theory
- possible world representation
- Ad-Hoc methods
- Search and control strategies: Concepts
- search problems
- uniformed or Blined search
- Backward and forward chaining
- Hybrid control structures
- Meta rules decision lattices
- Concurrency in control structures
- AND-OR NOT lattices
- Randomness in control structures
- Grammars for interpreting languages
- Rule based system implementation-backward chaining
- Virtual facts and catching
- Input/output coding
- Intermediate predicates
- Probability in rules
- Independence assumption and/or combination
- search- state operators
- search as graphical traversal search strategies
- Depth first and breadth first heuristics
- cost and evaluation functions
- Optimal path search
Unit 4 Knowledge Organization and Communication in Expert System
- Matching techniques: Need for matching
- matching problem
- partial matching
- Fuzzy matching
- RETE matching algorithm
- Knowledge organization- Indexing and retrieval techniques
- integration of knowledge in memory organization systems
- communication and Expert systems
- Overview of Linguistics
- Basic passim techniques
- semantic analysis and representation structures
- Natural language generation and system
Unit 5 Pattern Recognition and Learning Techniques
- Pattern recognition system: Understanding speech recognition
- Image transformation
- low level processing
- medium and high level processing
- vision system architecture
- Rule based system architecture
- knowledge acquisition and validation
- knowledge system building tools
- use of AI and ES in manufacturing and design
- types of learning- general learning model
- performance measures
- learning automate genetic algorithm
- learning by induction - EX, ID3, INDUCE systems
- Artificial Intelligence- Modern approach
- Russel (Stuart)
- Introduction to Artificial intelligence and Expert systems
- Dan W Patterson
- Introduction to Artificial intelligence
- Eugene Charniak
- Drew Mc Dermot
- Addison Wesley Longman. 4
- Artificial intelligence and the design of expert systems
- The Benjamin Cummins Publishing Co.
- Artificial intelligence An Engineering Approach
- Robert J Schalkoff
- McGraw Hill
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