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