Module 1: Optimization Techniques (12 Hours)
- 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)

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Module 2: Introduction to Machine Learning (22 Hours)
- 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

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Module 3: Principal Component Analysis (PCA) (4 Hours)
- Dimension Reduction by PCA

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

At the end of the course, students will be able to

CO1: Analyze different types of optimization problems and apply appropriate optimization techniques to solve them.

CO2: Implement single and multi-layer perceptrons for input-output mapping and classification problems.

CO3: Apply clustering techniques to categorize datasets based on patterns and relationships.

CO4: Utilize Principal Component Analysis (PCA) for dimensionality reduction in large-scale data processing.

CO5: Develop and evaluate machine learning models for real-world industrial applications.

Boyd and Vandenberghe, Convex Optimization, Cambridge University Press

S. Haykin, Neural Networks: A Comprehensive Foundation, Pearson

T. J. Ross, Fuzzy Logic with Engineering Application, John Wiley and Sons

V. Kecman, Learning & Soft Computing, Pearson