Syllabus

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.

Basic concepts of machine learning, inductive learning, decision tree learning, semi-supervised learning, ensemble learning, clustering, artificial neural networks, support vector machines, bayesian learning, deep learning, Convolution neural network, accuracy assessment