Foundations of Artificial Intelligence VI

Machine Learning basics

This course is part of the course series "Foundations of Artificial Intelligence" that covers a variety of algorithms and methods that are of central importance in AI and of major practical relevance.

4 weeks à 3 hours


The course "Foundations of Artificial Intelligence VI" introduces introduces the mathematical foundations of supervised learning and discusses two learning algorithms in depth that are widely used in practice: decision tree learning and neural networks.  

Participants learn how to apply these algorithms to practical problems and how to evaluate the quality of a learned classifier. The course concludes with a discussion of the risks and challenges of machine learning.  

The course is based on the textbook by Stuart Russell and Peter Norvig: Introduction to Artificial Intelligence, 3rd edition, 2012 or 4th edition, 2020. The 3rd edition is also available in German: Stuart Russell und Peter Norvig: Künstliche Intelligenz - Ein Moderner Ansatz, 3. aktualisierte Auflage, Pearson 2012. 

The course is held in English language with German subtitles. 

Which topics will be covered?


Module Introduction to Machine Learning 

  • The learning agent revisited 
  • Types of learning 
  • Ockham´s razor 


Module Decision Tree Learning 

  • Boolean decision trees 
  • Restaurant example 
  • Overfitting 
  • Theoretical properties 
  • Recursive decision tree learning algorithm 
  • Information Gain and Entropy 
  • Entropy of a Boolean variable 
  • Information Gain 
  • Use in Decision Tree Learning 


Module Neural Networks 

  • Introduction 
  • History of neural networks 
  • McCulloch-Pitts “Unit” Model of a Neuron 
  • Activation functions 
  • Feed-forward networks 
  • Single layer feed-forward networks 
  • Single layer feed-forward networks 
  • Theoretical properties and limitations 
  • Backpropagation algorithm 
  • Take right-turn example 


Module Evaluation, Challenges and Risks 

  • Machine learning workflow 
  • Training and test data 
  • Learning curve 
  • K-fold cross validation 
  • Evaluating a Boolean classifier 
  • False Positives and false negatives 
  • Accuracy, recall, specificity, precision 
  • F1 Score 
  • Risks and challenges of machine learning 
  • Statistical vs. causal models 
  • Wrong predictions with high confidence 
  • Universal Adversarial Perturbations 


What will I achieve?

By the end of the course, you‘ll be able to 

  • understand the theoretical foundations of machine learning algorithms, 
  • evaluate a learned classifier 
  • apply decision tree learning to practical problems 
  • apply neural network learning to practical problems 
  • understand challenges and risks of machine learning algorithms 

Which prerequisites do I need to fulfill?


Who is offering this course?

Jana Koehler
Prof. Dr. Jana Koehler
Deutsches Forschungszentrum für Künstliche Intelligenz
Universität des Saarlandes
Artificial Intelligence Group / Saarland University
Dr. Sophia Saller
Dr. Sophia Saller
Deutsches Forschungszentrum für Künstliche Intelligenz
Annika Engel
Annika Engel
Deutsches Forschungszentrum für Künstliche Intelligenz

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What else should I know?

Für die Inhalte der Lernangebote sind die Lernangebotserstellenden verantwortlich.

Online course
Record of Achievement
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