Soft Computing
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Follow on Instagram for UpdatesSoft Computing Syllabus — Unit-wise Content
The official GTU curriculum for Soft Computing (subject code 3174603) as prescribed for CS-DS · Semester 7 divides the course into 6 units, covering 31 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 (3174603)Unit 1 Unit 1
- INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS: Evolution of Computing: Soft Computing Constituents
- From Conventional AI to Computational Intelligence: Machine Learning Basics
Unit 2 Unit 2
- FUZZY LOGIC: Fuzzy Sets
- Operations on Fuzzy Sets
- Fuzzy Relations
- Membership Functions: Fuzzy Rules and Fuzzy Reasoning
- Fuzzy Inference Systems
- Fuzzy Expert Systems
- Fuzzy Decision Making
Unit 3 Unit 3
- NEURAL NETWORKS: Machine Learning Using Neural Network
- Adaptive Networks
- Feed forward Networks
- Supervised Learning Neural Networks
- Radial Basis Function Networks : Reinforcement Learning
- Unsupervised Learning Neural Networks
- Adaptive Resonance architectures
- Advances in Neural networks
Unit 4 Unit 4
- GENETIC ALGORITHMS: Goals of optimization
- comparison with traditional methods
- Terminology in GA – strings
- parameter string
- data structures
- coding fitness function
- applications of GA in Machine Learning : Machine Learning Approach to Knowledge Acquisition
Unit 5 Unit 5
- Matlab/Python Lib: Introduction to Matlab/Python
- Arrays and array operations
- Functions and Files
- Study of neural network toolbox and fuzzy logic toolbox
- Simple implementation of Artificial Neural Network and Fuzzy Logic
Unit 6 Unit 6
- Recent Trends in various classifiers
- neural networks and genetic algorithm Subject Code: 3720217
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