Undergraduate courses

Manufacturing Processes I

Casting processes, Joining processes, bulk and Sheet Forming processes, Powder processing, Polymers and Composites, additive manufacturing

Postgraduate electives

Micromechanics of composite

Tensor calculus and fundamental Equations of elasticity, Green’s function, inclusions and inhomogeneities (Eshelby tensor), Effective elastic moduli of composites (Voigt and Reuss bounds, Self-consistent method, Generalized self-consistent method, Differential self-consistent method, Mori-Tanaka method, Perturbation method, Periodic structure, viscoelastic and Plastic behavior of Composites.

Advanced Stereology and Microstructural Analysis

Concepts and language of stereology; geometrical probability; fundamental operations in stereology; averaging with respect to orientation; basic stereological parameters on true 2-D sections and thick sections; topological parameters of microstructure; error analysis; numerical density and size distribution of particles and grains of various shapes and sizes; stereological analysis of anisotropic microstructures; Application of digital Image analysis for quantitative analysis of microstructures and mathematical models in stereology

Engineering Data Mining and Applications

Introduction to statistics, Introduction to Machine Learning (Supervised, Unsupervised, Reinforcement Learning, model parameters and hyperparameters), regression (Linear, Multiple Linear, Non-linear, Logistic,  k-Nearest Neighbors Bias-Variance Tradeoff, model performance evaluation, Loss function, sub-set selection); Classification (Logistic, k-Nearest Neighbors , Linear Discriminant Analysis, decision trees, random forest)   Evaluating the performance of a classifier (ROC Curve, Confusion Matrix); Support Vector Machine (linear and non-linear kernels), dimensionality reduction (PCA),        k-Means clustering, hierarchical clustering; Introduction to Big data, ANN, Deep Learning.

PG Certificate Courses for industry:

Data Analytics & Machine Learning using Python (5 days)

Deep learning: Theory and practice (5 days)

Theory and application of Finite Element Methods for structural analysis (6 days)

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