Teaching
How to study ML at the Department of Statistics
Flipped Classroom Concept
Open Teaching Projects
- Automated Machine Learning
- Introduction to Machine Learning, also as MOOC on KI-Campus
- Introduction to Deep Learning
- Optimization for Machine Learning
- Deep Learning for Natural Language Processing
- Interpretable Machine Learning
Student Research Project
Student research projects are intended to provide insight into both the research process and the day-to-day work of academic scientists. In addition to gaining hands-on research experience, students may have the opportunity to contribute to a scientific publication, provided the project yields results suitable for submission to an appropriate venue. For research projects of appropriate scope and academic quality, it is generally possible for students enrolled in the Master’s program Statistics and Data Science to earn ECTS credits, e.g., 3 ECTS toward the module Selected Topics in … or 6 ECTS toward the module Current Research in ….
If you are a motivated undergraduate student and interested in working on a research project, please contact the PostDoc/Lead of the research group that best aligns with your interests. Be sure to include your current transcript of records as well as your Bachelor’s degree transcript, as we will prioritize students with a strong and suitable background for the respective project.
For more information please visit the official web page Studentische Forschungsprojekte (Lehre@LMU)
Exam Archive
Lectures
WS 2025/26
- Programmieren mit statistischer Software (R)
- Deep Learning for Natural Language Processing
- Nebenfach (BA): Grundlagen der Statistik für Studierende der Wirtschaftswissenschaften
- Nebenfach (BA): Statistik I für Studierende der Wirtschaftswissenschaften
SS 2025
- Supervised Learning / Predictive Modelling
- Applied Machine Learning
- Advanced Machine Learning / Interpretable Machine Learning
- Algorithms and Data Structures
- Einführung in Python für Data Science
- Introduction to Machine Learning (Major Statistics)
- Nebenfach (BA): Grundlagen der Statistik für Studierende der Wirtschaftswissenschaften
- Nebenfach (BA): Statistik II für Studierende der Wirtschaftswissenschaften
- Seminar (BA): Probabilistic ML
- Seminar (BA): Data Science Competitions
- Seminar (MA): Advances in Automated Machine Learning (with a focus on foundation models)
- Seminar (MA): Advances in Variational Autoencoding
- Seminar (MA): Foundation Models in e-Health: Real-world Applications and Innovations
- Deep Learning (MA)
- Applied Deep Learning (MA)
WS 2024/25
- Programmieren mit statistischer Software (R)
- Applied Deep Learning with TensorFlow and PyTorch
- Deep Learning for Natural Language Processing
- Einführung in die Statistische Software (R)
- Introduction to Machine Learning (Minor Statistics / AIM)
- Machine Learning und Deep Learning mit Python
- Nebenfach (BA): Grundlagen der Statistik für Studierende der Wirtschaftswissenschaften
- Nebenfach (BA): Statistik I für Studierende der Wirtschaftswissenschaften
- Seminar (BA): Trustworthy ML
- Seminar (MA): Advances in Tabular Machine Learning
- Seminar (MA): Causal Discovery
- Seminar (MA): Foundation Models in Action: Practical Techniques and Applications
- Automated Machine Learning
- Optimization in Machine Learning
- Supervised Learning / Predictive Modelling
- Multivariate Statistics (for ESG Data Science)
SS 2024
- Supervised Learning/Predictive Modelling
- Seminar Automated Machine Learning
- Applied Machine Learning
- Nebenfach (BA): Statistik II für Studierende der Wirtschaftswissenschaften
- Introduction to Machine Learning (Major Statistics / Computer Science / Business Mathematics)
- Machine Learning und Deep Learning mit Python
WS 2023/24
- Introduction to Machine Learning (Minor Statistics / AIM)
- Programmieren mit statistischer Software (R)
- Einführung in die Statistische Software (R)
- Supervised Learning a.k.a. Predictive Modelling
- Optimization
- Deep Learning for Natural Language Processing
- Automated Machine Learning
- Applied Deep Learning
- Lifetime Data Analysis
- Advanced Programming (R)
- Multivariate Statistics
- Seminar (BA): Fairness im Maschinellen Lernen
- Seminar (MA): Multi-objective Optimization and Machine Learning
- Seminar (MA): Forschungsseminar Statistical Learning and Data Science
- Seminar (MA): Machine Learning Meets Causality
- Nebenfach (BA): Statistik I für Studierende der Wirtschaftswissenschaften
SS 2023
- Introduction to Machine Learning
- Supervised Learning (aka Predictive Modelling)
- Advanced Machine Learning
- Applied Deep Learning with TensorFlow and PyTorch
- Deep Learning
- Machine Learning and Deep Learning with Python
- Statistical Learning Theory
- Block Course MLOps
WS 2022/23
- Introduction to Machine Learning
- Optimization
- Deep Learning for Natural Language Processing
- Programmieren mit statistischer Software (R)
- Advanced Programming (R)
- Einführung in die Statistische Software (R)
- Supervised Learning a.k.a. Predictive Modelling
- Innovationslabor Big Data Science
- Seminar: Unsupervised Deep Learning
- Automated Machine Learning
- Multivariate Statistics
SS 2022
- Introduction to Machine Learning
- Supervised Learning (aka Predictive Modelling)
- Advanced Machine Learning
- Applied Deep Learning with TensorFlow and PyTorch
- Seminar: Unsupervised Deep Learning
- Seminar: Multimodal Deep Learning
- Deep Learning
- Machine Learning and Deep Learning with Python
- Programmieren mit statistischer Software (R)
WS 2021/22
- Supervised Learning (aka Predictive Modelling)
- Introduction to Machine Learning
- Optimization
- Lifetime Data Analysis
- Deep Learning
- Automated Machine Learning
- Seminar: XAI
- Innovationslabor Big Data Science
- Statistische Software (R)
- Seminar: Uncertainty Quantification in Deep Learning
SS 2021
- Predictive Modeling
- Programmieren mit statistischer Software (R)
- Statistische Software (R)
- Data Science in der Praxis: Machine Learning und Deep Learning mit Python
- Applied Deep Learning with Tensorflow and Pytorch
- Seminar: Unsupervised Learning
- Seminar: Causality
- Seminar: Uncertainty Quantification in Deep Learning (cancelled)
WS 2020/21
- Deep Learning
- Introduction to Machine Learning
- Multivariate Statistics
- AutoML: Automated Machine Learning
- CIM1 - Statistical Computing
- Lifetime Data Analysis
- Innovation Lab Big Data Science
- Seminar: Ethics in AI
- Seminar: Time-to-event Machine Learning
- Seminar: Manifold Learning - Modern Approaches towards Dimensionality Reduction
SS 2020
- Predictive Modeling
- Statistische Software (R)
- Programmieren mit statistischer Software (R)
- Modern Approaches in Natural Language Processing
- Seminar Graphical Models and Causality
WS 2019/20
- Multivariate Statistics
- Innovationslabor Big Data Science
- Introduction to Deep Learning
- Introduction to Machine Learning
- CIM1 - Statistical Computing
SS 2019
- Introduction to Machine Learning
- Predictive Modeling
- Fortgeschrittene Computerintensive Methoden
- Statistische Software (R)
- Programmieren mit statistischer Software (R)
WS 2018/19
- Multivariate Statistics
- Computerintensive Methoden – Statistical Computing
- Innovationslabor Big Data Science
- Introduction to Deep Learning
- Introduction to Machine Learning
SS 2018
WS 2017/18
- Introduction to Machine Learning
- Innovationslabor Big Data Science
- Introduction to Deep Learning
- CIM1 - Statistical Computing
- Multivariate Statistics
SS 2017
WS 2016/17
- CIM1 - Statistical Computing
- Multivariate Statistics for Data Science
- Master Seminar: Introduction to Deep Learning
SS 2016
- Fortgeschrittene Computerintensive Methoden
- Seminar: Variablen- und Modellselektion
- Stochastik und Statistik
WS 2015/16
SS 2015
- Fortgeschrittene Computerintensive Methoden
- Stochastik und Statistik
Bernd’s Previous Teaching in Dortmund
WS 2014/15
- Efficient and parallel programming with R
- Wahrscheinlichkeitsrechnung und Mathematische Statistik für Informatiker
Earlier
- Übung Computergestützte Statistik
- Data Mining Cup (SS09, SS10, SS11)
- Seminar Support Vector Machines und Kernel-Methoden (WS1011)
- Übung Computergestützte Statistik (WS0910)
- Übung Statistik V (WS0809)