NOC:Machine Learning


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