Leibniz MMS AI School 2025#

This page contains training materials for the AI School of the Leibniz Research Network Modelling and Simulation (MMS) at the Leibniz Institute of Surface Engineering (IOM) in Leipzig.

Note: This page is under heavy construction

Target audience#

The notebooks are written for scientists with basic knowledge of Python programming and are beginners in machine learning (ML) and artificial intelligence (AI). By the end of the training, active participants will have acquired fundamental knowledge in ML and AI. They will then know how to apply suitable ML/AI techniques to automatically process, analyze, and visualize data.

Trainer / Speaker#

Preliminary Program#


Monday:
12:00–13:00: Arrival of participants
13:00–14:00: Introduction round of instructors and participants
14:00–15:00: Introduction to the technical setup for the workshop and JupyterLab for all participants
15:00-15:15: Coffee break
15:15–18:00: Machine Learning Techniques: Regression

Tuesday:
09:00–11:00: Data Wrangling Part 1
11:00-11:15: Coffee break
11:15-12:00: Data Wrangling Part 2
12:00–13:00: Machine Learning Techniques: Classification
13:00–14:00: Lunch break
14:00–16:00: Machine Learning Techniques: Clustering
19:00–22:00: Conference dinner

Wednesday:
09:00–11:00: Deep Learning
11:00-11:15: Coffee break
11:15–13:00: Data Visualization in a JupyterLab Environment
13:00–14:00: Lunch break
15:00–17:00: City tour

Thursday:
09:00–11:00: Language Models
11:00-11:15: Coffee break
11:15–13:00: Breakout sessions on specialized topics
13:00–14:00: Lunch Break
14:00–16:00: Breakout sessions on specialized topics
16:00-16:15: Coffee break
16:15–18:00: Breakout sessions on specialized topics
From 19:00: Visit to the Festival of Lights in Leipzig commemorating the peaceful revolution of 1989

Covered Python libraries#

  • bia-bob: AI-assisted Code Generation

  • numpy: Basic numeric Processing

  • scipy

  • scikit-image: Scientific Image Processing

  • scikit-learn: Machine Learning Library

  • seaborn

  • pytorch

  • stackview: An interactive nD image viewer for Jupyter Notebooks

Acknowledgements#

We reused materials

  • about data visualization originally made by Marie-Sophie von Braun and Jan Ewald, ScaDS.AI, shared under CC-BY 4.0.

We acknowledge the financial support by the Federal Ministry of Education and Research of Germany and by Sächsische Staatsministerium für Wissenschaft, Kultur und Tourismus in the programme Center of Excellence for AI-research „Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig“, project identification number: ScaDS.AI