Einführung in Machinelles Lernen in den Materialwissenschaften

What it is about

The course provides the fundamentals of machine learning with a focus on applications in materials science. You will learn typical methods (e.g., clustering, regression, classification, deep learning, Bayesian optimization), work with materials science datasets, and practice practical implementation in Python notebooks. In the end you will be able to select appropriate representations and methods, evaluate models, and carry out a small ML project in materials science.

What you will be able to do

• Insight into machine learning in materials science
• Overview of different ML methods
• Hands-on with Python notebooks
• Handling various materials datasets
• Fundamental data analysis of materials science data
• Selection of suitable material representations for ML
• Assessment of appropriate ML methods for concrete problems
• Evaluation and improvement of ML models
• Conducting an ML project in materials science
• Critical appraisal of methods and data analysis
• Identification of materials science questions that ML can solve
• Implications of presentations on the topic of ML in materials science

What the module consists of

• VorlesungVermittlung der theoretischen Grundlagen des maschinellen Lernens
• ComputerübungenPraktische Umsetzung und Training mit Python-Notebooks
• ProjektarbeitAnwendung der Methoden auf materialwissenschaftliche Fragestellungen

Teaching method

• VorlesungenErklären der Konzepte und Methoden
• ComputerübungenPraktische Übung und Festigung der Implementierung in Python
• ProjektarbeitVertiefung durch eigenständige Anwendung auf reale Probleme