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FoAI VI
Kursankündigung
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.

Start
Summer 2022
Umfang
4 weeks à 3 hours
Sprache
Englisch

Overview

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?

None.

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

Für die Inhalte der Lernangebote sind die Lernangebotserstellenden verantwortlich.

What else should I know?

Für die Inhalte der Lernangebote sind die Lernangebotserstellenden verantwortlich.

Lernformat:
Online course
Kenntnislevel:
Beginner
Lizenz
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