Private: BE COMPS SEM 7 – ARTIFICIAL INTELLIGENCE & SOFT COMPUTING

Module 5 – Artificial Neural Network
12 Topics
5.1.a Introduction to Artificial Neural network
5.1.b Fundamental concept
5.1.c Basic Models of Artificial Neural Networks
5.1.d Important Terminologies of ANNs
5.2.a Neural Network Architecture: Perceptron
5.2.b Single layer Feed Forward ANN
5.2.c Multilayer Feed Forward ANN
5.2.d Activation functions
5.2.e Supervised Learning: Delta learning rule
5.2.f Back Propagation algorithm
5.2.g Un-Supervised Learning algorithm: Self Organizing Maps
5.1.e McCulloch-Pitts Neuron
Module 6 – Expert System
5 Topics
6.1 Hybrid Approach – Fuzzy Neural Systems
6.2.a Expert system : Introduction
6.2.b Characteristics
6.2.c Architecture
6.2.d Stages in the development of expert system
Module 1 -Introduction to Artificial Intelligence(AI) and Soft Computing
9 Topics
1.1 Introduction and Definition of Artificial Intelligence
1.2.a Intelligent Agents : Agents and Environments
1.2.c Nature of Environment
1.2.d Structure of Agent
1.2.e Types of Agent
1.3.a Soft Computing: Introduction of soft computing
1.3.b Soft computing vs. hard computing
1.3.c Various types of soft computing techniques
1.2.b Rationality
Module 2 – Problem Solving
9 Topics
2.1.a Problem Solving Agent
2.1.b Formulating Problems
2.1.c Example Problems
2.2.a Uninformed Search Methods: Depth Limited Search
2.2.b Depth First Iterative Deepening (DFID)
2.2.c Informed Search Method: A* Search
2.3.a Optimization Problems: Hill climbing Search
2.3.b Simulated annealing
2.2.c Genetic algorithm
Module 3 – Knowledge, Reasoning and Planning
9 Topics
3.1.a Knowledge based agents
3.2.a First order logic: syntax and Semantic
3.2.b Knowledge Engineering in FOL Inference in FOL : Unification
3.2.c Forward Chaining
3.2.d Backward Chaining and Resolution
3.3.a Planning Agent
3.3.b Types of Planning: Partial Order
3.3.c Hierarchical Order
3.3.d Conditional Order
Module 4 – Fuzzy Logic
8 Topics
4.2.a Fuzzy Logic: Fuzzy Logic basics
4.2.b Fuzzy Rules and Fuzzy Reasoning
4.3.a Fuzzy inference systems: Fuzzification of input variables
4.3.b Defuzzification and fuzzy controllers
4.1.a Introduction to Fuzzy Set: Fuzzy set theory
4.1.b Fuzzy set versus crisp set
4.1.c Crisp relation & fuzzy relations
4.1.d Membership functions
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6.2.a Expert system : Introduction

Private: BE COMPS SEM 7 – ARTIFICIAL INTELLIGENCE & SOFT COMPUTING Module 6 – Expert System 6.2.a Expert system : Introduction
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