Machine Learning

By Prof. Dr. Carl Gustaf Jansson

KTH Royal Insitute of technology in Stockholm

Week 1

• 
  Introduction to the Machine Learning Course
  
• 
  Foundation of Artificial Intelligence and Machine Learning
  
• 
  Intelligent Autonomous Systems and Artificial Intelligence
  
• 
  Applications of Machine Learning
  
• 
  Tutorial for week01
  

Week 2

• 
  Characterization of Learning Problems
  
• 
  Objects, Categories and Features
  
• 
  Feature related issues
  
• 
  Scenarios for Concept Learning
  
• 
  Tutorial for week02
  

Week 3

• 
  Forms of Representation
  
• 
  Decision Trees
  
• 
  Bayes (ian) Belief Networks
  
• 
  Artificial Neural Networks
  
• 
  Genetic algorithm
  
• 
  Logic Programming
  
• 
  Tutorial for week03
  

Week 4

• 
  Inductive Learning based on Symbolic Representations and Weak Theories
  
• 
  Generalization as Search – Part 01
  
• 
  Generalization as Search – Part 02
  
• 
  Decision Tree Learning Algorithms – Part 01
  
• 
  Decision Tree Learning Algorithms – Part 02
  
• 
  Instance Based Learning – Part 01
  
• 
  Instance Based Learning – Part 02
  
• 
  Cluster Analysis
  
• 
  Tutorial for week04
  

Week 5

• 
  Machine Learning enabled by Prior Theories
  
• 
  Explanation Based Learning
  
• 
  Inductive Logic Programming
  
• 
  Reinforcement Learning – Part 01 Introduction
  
• 
  Reinforcement Learning – Part 02 Learning Algorithms
  
• 
  Reinforcement Learning – Part 03 Q – Learning
  
• 
  Case – Based Reasoning
  
• 
  Tutorial for week05
  

Week 6

• 
  Fundamentals of Artificial Neural Networks – Part1
  
• 
  Fundamentals of Artificial Neural Networks – Part2
  
• 
  Perceptrons
  
• 
  Model of Neuron in an ANN
  
• 
  Learning in a Feed Forward Multiple Layer ANN – Backpropagation
  
• 
  Recurrent Neural Networks
  
• 
  Hebbian Learning and Associative Memory
  
• 
  Hopfield Networks and Boltzman Machines – Part 1
  
• 
  Hopfield Networks and Boltzman Machines – Part 2
  
• 
  Convolutional Neural Networks – Part 1
  
• 
  Convolutional Neural Networks – Part 2
  
• 
  DeepLearning
  
• 
  Assignments for week06
  

Week 7

• 
  Tools and Resources
  
• 
  Interdisciplinary Inspiration
  

Week 8

• 
  Preparation for Exam and Example of Applications