Course Details
Subject {L-T-P / C} : EE6243 : Soft Computing Techniques { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Ananyo Sengupta
Syllabus
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.
– Unsupervised Learning: Clustering by Self-Organizing Maps
• Support Vector Machine
3. Principal Component Analysis (PCA)
• Dimension Reduction by PCA
Course Objectives
- To learn different types of optimization techniques.
- To learn structure, training methods and applications of artificial neural networks.
- To design fuzzy controllers/fuzzy rule based systems
Course Outcomes
At the end of the course, students will be able to <br /> <br />CO1. Identify types of optimization problems and solve them with appropriate techniques. <br />CO2. Solve the input-output mapping and classification problems using single and multi-layer Perceptrons. <br />CO3: Perform clustering operations for a given set of data. <br />CO4: Implement dimension reduction technique for large-scale data. <br />CO5: Implement various machine learning algorithms for industry applications.
Essential Reading
- Boyd and Vandenberghe, Convex Optimization, Cambridge University Press
- S. Haykin, Neural Networks: A Comprehensive Foundation, Pearson
Supplementary Reading
- T. J. Ross, Fuzzy Logic with Engineering Application, John Wiley and Sons
- V. Kecman, Learning & Soft Computing, Pearson