Einführung in maschinelles Lernen

Einführung in Maschinelles Lernen (Introduction to machine learning)

Overview

Description

Contents:

• Python for Machine Learning,
• Introduction to Probability and statistics for Machine Learning,
• Introduction to Optimization for Machine Learning,
• Linear regression,
• Linear classification,
• Clustering,
• Dimensionality reduction,
• Introduction to Deep Learning.

Contact

Prof. Dr. Michel Besserve

Qualification

After successfully completing this module, students should be able to understand and correctly apply basic concepts of machine learning, master elementary tools for analysing the performance of machine learning approaches, recognise the most important limitations of machine learning methods, propose strategies to overcome such limitations.

Examination

1 examination: written exam, 90 minutes, or oral exam, 30 minutes, or take-home exam
1 academic achievement: 50% of the exercises must be passed

References

• Understanding Machine Learning, Shalev-Schwartz & Ben-David, 2014
• Learning Theory from First Principles, Bach, 2024
• Deep Learning, Goodfellow et al., 2016
• Mathematical Theory of Deep Learning, Petersen & Zech, 2024
• Mathematics for Machine Learning, Deisenroth et al., 2020
• Neural Networks and Deep Learning, Aggarwal, 2023 (2nd edition)
• Deep Learning Architectures, Calin, 2020