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Convolutional Neural Networks - Part 2
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Classroom Contents
Machine Learning
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- 1 Introduction to the Machine Learning Course
- 2 Foundation of Artificial Intelligence and Machine Learning
- 3 Intelligent Autonomous Systems and Artificial Intelligence
- 4 Characterization of Learning Problems
- 5 Objects, Categories and Features
- 6 Feature related issues
- 7 Forms of Representation
- 8 Decision Trees
- 9 Bayes (ian) Belief Networks
- 10 Artificial Neural Networks
- 11 Genetic algorithm
- 12 Inductive Learning based on Symbolic Representations and Weak Theories
- 13 Generalization as Search - Part 01
- 14 Generalization as Search - Part 02
- 15 Decision Tree Learning Algorithms - Part 01
- 16 Decision Tree Learning Algorithms - Part 02
- 17 Instance Based Learning - Part 01
- 18 Instance Based Learning - Part 02
- 19 Machine Learning enabled by Prior Theories
- 20 Explanation Based Learning
- 21 Inductive Logic Programming
- 22 Reinforcement Learning - Part 01 Introduction
- 23 Reinforcement Learning - Part 02 Learning Algorithms
- 24 Reinforcement Learning - Part 03 Q - Learning
- 25 Fundamentals of Artificial Neural Networks - Part1
- 26 Fundamentals of Artificial Neural Networks - Part2
- 27 Perceptrons
- 28 Model of Neuron in an ANN
- 29 Learning in a Feed Forward Multiple Layer ANN - Backpropagation
- 30 Recurrent Neural Networks
- 31 Hebbian Learning and Associative Memory
- 32 Hopfield Networks and Boltzman Machines - Part 1
- 33 Hopfield Networks and Boltzman Machines - Part 2
- 34 Convolutional Neural Networks - Part 1
- 35 Convolutional Neural Networks - Part 2
- 36 Tools and Resources
- 37 Interdisciplinary Inspiration
