Machine Learning for Data Science

Machine Learning for Data Science

Overview

Description

• Foundations of supervised learning
• Optimization for ML
• Unsupervised learning
• Neural networks
• Deep learning
• Deep generative models
• Some ML weaknesses
• Interpretable-explainable AI

Qualification goals

Upon successful completion of this module, students should be able to understand and correctly apply fundamental concepts of machine learning, master elementary tools for the analysis of machine learning approaches, recognize the key limitations of machine learning methods, and propose strategies to overcome these limitations.

Proof of Performance

• 1 Prüfungsleistung: Klausur, 90 Minuten, oder mündliche Prüfung, 30 Minuten, oder Take-Home-Exam
• 1 Studienleistung: 50% der Übungsaufgaben müssen bestanden sein

Literature

• 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