Spatial data representation – discrete Euclidean plane/ geometric domain/ discritisation of arcs – spatial object domain: vector data models: spaghetti, arc – node , DCEL – field based model: tasseled representation, triangulation, voronoi, Delaunay triangulation – geometric algorithms, topological  and set based algorithms – network analysis, traveling salesperson algorithm – spatial analysis: interpolation methods, deterministic, stochastic, geostatistics, spatial autocorrelation, semi-variogram, kriging, types of kriging – uncertainty and its assessment.

Kernel Methods:  Introduction to metric space, vector space, normed space, inner product space; RKHS; Learning theory;  SVM for classification & regression; implementation techniques of SVM;   kernel ridge regression;  kernel density estimation;  kernel PCA; kernel online learning.Random forest, Genetic algorithms, ant colony optimization

Spectral Clustering; model based clustering, Expectation Maximization; Independent Component Analysis; Hidden Markhov models;  Factor Analysis; introduction to Graphical models & Sampling Methods.