Course Details

Subject {L-T-P / C} : EE6243 : Soft Computing Techniques {3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Ananyo Sengupta


1. Optimization Techniques
• Preliminary Mathematics: Matrix Calculus, Taylor Series Expansion, Convex Set and Convex Function
• Convex Optimization: Unconstrained Optimization by Steepest Descent Method, Newton’s Method and LMDN Method, Conjugate Gradient Method, Constrained Optimization
• Nonconvex Optimization: Genetic Algorithm (GA), Particle Swarm Optimization (PSO)

2. Introduction To Machine Learning
• Regression: Linear and Polynomial Regression, Classification by Logistic Regression
• Artificial Neural Network:
– Supervised Learning: Input-Output Mapping and Classification by Adaptive Linear Model, Multi-Layer Perceptron: Back-Propagation Learning, Radial Basis Function Neural Network, Recurrent Neural Network
– Unsupervised Learning: Clustering by Self-Organizing Maps
• Support Vector Machine

3. Principal Component Analysis (PCA)
• Dimension Reduction by PCA

Course Objectives

  1. To learn different types of optimization techniques.
  2. To learn structure, training methods and applications of artificial neural networks.
  3. To design fuzzy controllers/fuzzy rule based systems

Course Outcomes

At the end of the course, students will be able to
1. Identify type of optimization problems and solve them with appropriate technique.
2. Solve the input-output mapping and classification problems using single and multi-layer Perceptron
3. Understand dimension reduction techniques using PCA
4. Design fuzzy rule base and fuzzy controller.

Essential Reading

  1. S. S. Rao, Engineering Optimization: Theory and Practice, John Wiley & Sons
  2. S. Haykin, Neural Networks: A Comprehensive Foundation, Pearson

Supplementary Reading

  1. T. J. Ross, Fuzzy Logic with Engineering Application, John Wiley and Sons
  2. V. Kecman, Learning & Soft Computing, Pearson