This repo will contain PPT slideds used by the professor Sudeshna Sarkar in the NPTEL course Introduction to machine learning.
Introduction: Basic definitions, types of learning, hypothesis space and inductive bias, evaluation, cross-validation
Linear regression, Decision trees, overfitting
Instance based learning, Feature reduction, Collaborative filtering based recommendation
Probability and Bayes learning
Logistic Regression, Support Vector Machine, Kernel function and Kernel SVM
Neural network: Perceptron, multilayer network, backpropagation, introduction to deep neural network
Computational learning theory, PAC learning model, Sample complexity, VC Dimension, Ensemble learning
Clustering: k-means, adaptive hierarchical clustering, Gaussian mixture model