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

Co-ordinators : Prof. Henrik Bostrom, Prof. Fredrik Kilander, Prof. Carl Gustaf Jansson


Lecture 1 - Introduction to the Machine Learning Course

Lecture 2 - Foundation of Artificial Intelligence and Machine Learning

Lecture 3 - Intelligent Autonomous Systems and Artificial Intelligence

Lecture 4 - Applications of Machine Learning

Lecture 5 - Tutorial for week 1

Lecture 6 - Characterization of Learning Problems

Lecture 7 - Objects, Categories and Features

Lecture 8 - Feature related issues

Lecture 9 - Scenarios for Concept Learning

Lecture 10 - Tutorial for week 2

Lecture 11 - Forms of Representation

Lecture 12 - Decision Trees

Lecture 13 - Bayes (ian) Belief Networks

Lecture 14 - Artificial Neural Networks

Lecture 15 - Genetic algorithm

Lecture 16 - Logic Programming

Lecture 17 - Tutorial for week 3

Lecture 18 - Inductive Learning based on Symbolic Representations and Weak Theories

Lecture 19 - Generalization as Search - Part 1

Lecture 20 - Generalization as Search - Part 2

Lecture 21 - Decision Tree Learning Algorithms - Part 1

Lecture 22 - Decision Tree Learning Algorithms - Part 2

Lecture 23 - Instance Based Learning - Part 1

Lecture 24 - Instance Based Learning - Part 2

Lecture 25 - Cluster Analysis

Lecture 26 - Tutorial for week 4

Lecture 27 - Machine Learning enabled by Prior Theories

Lecture 28 - Explanation Based Learning

Lecture 29 - Inductive Logic Programming

Lecture 30 - Reinforcement Learning - Part 1 Introduction

Lecture 31 - Reinforcement Learning - Part 2 Learning Algorithms

Lecture 32 - Reinforcement Learning - Part 3 Q-Learning

Lecture 33 - Case - Based Reasoning

Lecture 34 - Tutorial for week 5

Lecture 35 - Fundamentals of Artificial Neural Networks - Part 1

Lecture 36 - Fundamentals of Artificial Neural Networks - Part 2

Lecture 37 - Perceptrons

Lecture 38 - Model of Neuron in an ANN

Lecture 39 - Learning in a Feed Forward Multiple Layer ANN - Backpropagation

Lecture 40 - Recurrent Neural Networks

Lecture 41 - Hebbian Learning and Associative Memory

Lecture 42 - Hopfield Networks and Boltzman Machines - Part 1

Lecture 43 - Hopfield Networks and Boltzman Machines - Part 2

Lecture 44 - Convolutional Neural Networks - Part 1

Lecture 45 - Convolutional Neural Networks - Part 2

Lecture 46 - DeepLearning

Lecture 47 - Tutorial for week 6

Lecture 48 - Tools and Resources

Lecture 49 - Interdisciplinary Inspiration

Lecture 50 - Preparation for Exam and Example of Applications