NPTEL : NOC:Introduction to Machine Learning (Computer Science and Engineering)

Co-ordinators : Prof. S. Sarkar


Lecture 1 - Introduction

Lecture 2 - Different Types of Learning

Lecture 3 - Hypothesis Space and Inductive Bias

Lecture 4 - Evaluation and Cross-Validation

Lecture 5 - Tutorial - I

Lecture 6 - Linear Regression

Lecture 7 - Introduction to Decision Trees

Lecture 8 - Learning Decision Tree

Lecture 9 - Overfitting

Lecture 10 - Python Exercise on Decision Tree and Linear Regression

Lecture 11 - Tutorial - II

Lecture 12 - k-Nearest Neighbour

Lecture 13 - Feature Selection

Lecture 14 - Feature Extraction

Lecture 15 - Collaborative Filtering

Lecture 16 - Python Exercise on kNN and PCA

Lecture 17 - Tutorial - III

Lecture 18 - Bayesian Learning

Lecture 19 - Naive Bayes

Lecture 20 - Bayesian Network

Lecture 21 - Python Exercise on Naive Bayes

Lecture 22 - Tutorial - IV

Lecture 23 - Logistic Regression

Lecture 24 - Introduction Support Vector Machine

Lecture 25 - SVM : The Dual Formulation

Lecture 26 - SVM : Maximum Margin with Noise

Lecture 27 - Nonlinear SVM and Kennel Function

Lecture 28 - SVM : Solution to the Dual Problem

Lecture 29 - Python Exercise on SVM

Lecture 30 - Introduction

Lecture 31 - Multilayer Neural Network

Lecture 32 - Neural Network and Backpropagation Algorithm

Lecture 33 - Deep Neural Network

Lecture 34 - Python Exercise on Neural Network

Lecture 35 - Tutorial - VI

Lecture 36 - Introduction to Computational Learning Theory

Lecture 37 - Sample Complexity : Finite Hypothesis Space

Lecture 38 - VC Dimension

Lecture 39 - Introduction to Ensembles

Lecture 40 - Bagging and Boosting

Lecture 41 - Introduction to Clustering

Lecture 42 - Kmeans Clustering

Lecture 43 - Agglomerative Hierarchical Clustering

Lecture 44 - Python Exercise on kmeans clustering