Teaching
How to study ML at the Department of Statistics
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
Exam Archive
Lectures
WS 2024/25
- Programmieren mit statistischer Software (R)
- Applied Deep Learning with TensorFlow and PyTorch
- Deep Learning for Natural Language Processing
- 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): 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)