Artificial Intellgence and Soft Computing
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Follow on Instagram for UpdatesArtificial Intellgence and Soft Computing Syllabus — Unit-wise Content
The official GTU curriculum for Artificial Intellgence and Soft Computing (subject code 1716204) as prescribed for IOT · Semester 1 divides the course into 5 units, covering 43 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 (1716204)Unit 1 UNIT- I
- Introduction to Artificial Intelligence(AI) and Soft Computing Introduction and definition of Artificial Intelligence
- Intelligent Agents: Agents and Environments
- Nature of Environment
- Structure of Agent
- types of Agent
- Soft Computing: Introduction of soft computing
- soft computing Vs. hard computing
- various types of soft computing techniques
- Some applications of Soft computing techniques
Unit 2 UNIT-II
- Problem Solving by Intelligent Search Searching for solutions
- uninformed search strategies – Breadth-first search
- depth-first search
- Search with partial information (Heuristic search) Hill climbing
- AO* Wef-AY-2021-22
Unit 3 UNIT-III
- Fuzzy Logic Introduction to Fuzzy Set: Fuzzy set theory
- Fuzzy set Vs crisp set, Crisp relation & fuzzy relations
- membership functions
- Fuzzy Logic: Fuzzy Logic basics
- Fuzzy Rules and Fuzzy Reasoning
- Fuzzy inference systems: Fuzzification of input variables
- defuzzification and fuzzy controllers
Unit 4 UNIT-IV
- Artificial Neural Network Introduction –Fundamental concept - Basic Models of Artificial Neural Networks –Important Terminologies of ANNs - McCulloch-Pitts Neuron
- Neural Network Architecture: Perceptron
- Single layer Feedforward ANN, Multilayer Feed Forward ANN, Activation functions
- Supervised Learning: Delta learning rule
- Back Propagation algorithm
- Un-Supervised Learning algorithm: Self Organizing Maps
Unit 5 UNIT-V
- Genetic Algorithm Biological Background
- Traditional optimization and search techniques
- genetic algorithm and search space
- genetic algorithm vs. traditional algorithms
- basic terminologies
- simple genetic algorithm
- general genetic algorithm
- operators in genetic algorithm
- stopping condition for genetic algorithm flow
- constraints in genetic algorithm
- problem-solving using genetic algorithm
- the schema theorem
- classification of genetic algorithm
- Holland classifier systems
- genetic programming
- advantages and limitations and applications of genetic algorithm
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