Electromagnetic radiation and its interaction with matter, Spectral signatures, image formation remote sensors and platforms, resolutions, radiometric and geometric distortions, thermal remote sensing, spectral indices, classification techniques, image transformations, intensity transformations, spatial filtering, image formats, noise reduction, image segmentation.

Programming with Python: Introduction to Python, data types, file operations, object oriented programming. Programming with R: Introduction to R, string operations, data visualization.

Machine learning basics: capacity, overfitting and underfitting, hyperparameters and validation sets, bias & variance; PAC model; Rademacher complexity; growth function; VC-dimension; fundamental concepts of artificial neural networks; single layer perceptron classifier; multi-layer feed forward networks; single layer feed-back networks; associative memories; introductory concepts of reinforcement learning, Markhov decision process.

Introduction to fundamental linear algebra problems and their importance, computational difficulties using theoretical linear algebra techniques, review of core linear algebra concepts; introduction to matrix calculus; floating point representation; conditioning of problems and stability of algorithms; singular value decomposition and regularization theory.

Introduction to data mining concepts; linear methods for regression; classification methods: k- nearest neighbourclassifiers, decision tree, logistic regression, naive Bayes, Gaussian discriminant analysis; model evaluation & selection;unsupervised learning: association rules; apriori algorithm, FP tree, cluster analysis, self organizing maps, google page ranking; dimensionality reduction methods: supervised feature selection, principal component analysis; ensemble learning: bagging, boosting, AdaBoost; outlier mining; imbalance problem; multi class classification; evolutionary computat

Optimization: need for unconstrained methods in solving constrained problems, necessary conditions of unconstrained optimization, structure methods, quadratic models, methods of line search, steepest descent method; quasi-Newton methods: DFP, BFGS, conjugate-direction methods: methods for sums of squares and nonlinear equations; linear programming: simplex methods, duality in linear programming, transportation problem; nonlinear programming: Lagrange multiplier, KKT conditions, convex programing

Introduction to linear vector spaces, bases and dimension, inner product, orthogonality, Fourier series,orthogonal polynomials, Cauchy Schwartz inequality, eigenvalues, eigenvectors, Hermitian operators,unitary operators, discrete Fourier transform.