Lecture | Neuro-Mechanistic Modeling

Statistics Lab

Wed, 01 Apr 2026 00:00:00 +0000

Team

Prof. Dr. Verena Wolf (lecturer)
Pascal L. Held, Pascal Schumann (assistants)

Course Goals

This course serves as an introduction to the fundamentals of statistics and probability theory and covers most of the core principles of modern data analysis and predictive modeling:

Descriptive Statistics and Visualizations in Python
Probabilities and Discrete Random Variables
Continuous Random Variables and Laws of Large Numbers
Parameter Estimation
Statistical Testing
Computer Simulations and Monte Carlo Methods
Stochastic Processes

Format

The course follows an inverted classroom approach and is divided into three modules:

Descriptive Statistics, Probability & Random Variables
Distributions & Parameter Estimation
Statistical Testing

Each week consists of:

Monday lecture (GHH, Prof. Wolf): a large group session covering the week’s material, including worked examples from the exercise sheet.
Tutorials: small group sessions to work on the previous week’s exercises, discuss solutions, and take mini-tests.
Self-study with ALA: practice exercises with ALA, an AI chatbot tailored to the course content that guides you through problems and answers your questions.
Videos and materials: new learning materials for self-study, released weekly.

To be admitted to the final exam, students complete the exercises and mini-tests in the tutorials and submit several dialogues with ALA.

Organization

All information and materials are available in the CMS.

Reinforcement Learning

Wed, 01 Oct 2025 00:00:00 +0000

Team

Prof. Dr. Verena Wolf (lecturer)
Lisa Dargasz, Yannic Muskalla (assistants)

Reinforcement Learning

Reinforcement learning is an area of machine learning where the goal is to develop (near-)optimal policies for solving sequential decision-making problems. The policy is typically represented by an agent who learns to achieve a goal by interacting with the environment. RL is often seen as the third area of machine learning (in addition to supervised and unsupervised learning), in which training samples are generated as a result of the agent’s actions and interaction with the environment. In recent years, there have been remarkable successes in reinforcement learning research in both theoretical and applied fields. These successes are mostly the result of a new development in the field: representing policies by artificial neural networks allows us to solve much more complex decision problems.

Course Content

This course provides a broad introduction to reinforcement learning and its applications. You learn about Markov Decision Processes as the underlying formal framework for decision-making problems, as well as popular reinforcement learning algorithms such as Monte-Carlo methods, temporal difference methods, and different “deep” reinforcement learning approaches. We use the open-source Python library Gymnasium to train RL agents in different pre-built environments.

Recommended prerequisites are Programming 1, Programming 2 and basic knowledge of probability theory, as taught for example in Mathematics for Computer Scientists 3.

Format

The course is a 6 ECTS advanced lecture with weekly lectures, bi-weekly assignment sheets with theoretical and programming exercises, tutorials, office hours and a final exam. Submitting the assignment sheets is voluntary. Admission to the final exam requires passing a mid-term exam.

Literature

Richard S. Sutton and Andrew G. Barto: Reinforcement Learning: An Introduction
Csaba Szepesvári: Algorithms for Reinforcement Learning

Organization

All information and materials are available in the CMS.

Statistics Lab

Mon, 01 Apr 2024 00:00:00 +0000

Team

Prof. Dr. Verena Wolf (lecturer)
Emilia Ellsiepen, Joschka Groß, Felix Jahn (assistants)

Course Goals

This course serves as an introduction to the fundamentals of statistics and probability theory and covers most of the core principles of modern data analysis and predictive modeling:

Descriptive Statistics and Visualizations in Python
Probabilities and Discrete Random Variables
Continuous Random Variables and Laws of Large Numbers
Parameter Estimation
Statistical Testing
Computer Simulations and Monte Carlo Methods
Stochastic Processes

Format

A weekly recorded on-site lecture, with a video course and lecture notes as supplementary online resources.
Weekly on-site tutorials in German, English, or bilingual.
Written mini-tests in every second lecture, and graded exercise sheets prepared in groups of 3–4 students.
Exam admission requires at least 50% of the combined score of mini-tests (2/3) and exercise sheets (1/3). Students scoring above 90% receive an exam bonus.
A written on-site exam at the end of the semester.

Organization

All information and materials are available in the CMS.

Reinforcement Learning

Sun, 01 Oct 2023 00:00:00 +0000

Team

Prof. Dr. Verena Wolf (lecturer)
Felix Jahn, Joschka Groß (assistants)

Reinforcement Learning

Course Content

Recommended prerequisites are Programming 1, Programming 2 and basic knowledge of probability theory, as taught for example in Mathematics for Computer Scientists 3.

Format

The course is a 6 ECTS advanced lecture with weekly lectures, bi-weekly assignment sheets with theoretical and programming exercises, bi-weekly tutorials and office hours, and a 90-minute final exam. Admission to the exam requires 50% of the points of the assignment sheets.

Literature

Richard S. Sutton and Andrew G. Barto: Reinforcement Learning: An Introduction
Csaba Szepesvári: Algorithms for Reinforcement Learning

The lecture ran again in Winter 2025/26.