Lecturer: Prof. Dr.-Ing. Timm Faulwasser
BSc course | written exam | 6 LP

Get an introduction to Machine Learning (ML) by engineers for engineers! This lecture covers the following fundamentals:

• Formalization of ML
• Numerical optimization
• Statistics and stochastics
• Classification: K-nearest neighbours, support vector machines, and kernel methods
• Regression and Gaussian processes
• Neural networks and aspects of deep learning
• Ethical questions of ML and AI

In the exercises, you gain a deeper understanding of the concepts by implementing and applying ML methods hands-on using Python.

Interested? We are looking forward to see you in the lecture hall!

Lecturer: Prof. Dr.-Ing. Timm Faulwasser & Prof. Dr.-Ing. Annika Eichler
BSc course | written exam | 6 LP

This course offers an introduction to the fundamentals of optimal and predictive control. The course starts with an introduction to convex optimization, followed by an overview of discrete-time control system theory.

The optimal control chapter introduces widely used controller structures, such as (in)-finite horizon linear quadratic regulators (LQR) as well as optimization techniques like dynamic programming. Finally, linear-quadratic model predictive control (LQ-MPC) is introduced.

In addition to the lectures, various exercise tasks are provided to deepen the understanding of the subject matter. Several of these include programming exercises that will be applied to the real segway system at the end of the lecture period.

Lecturer: Prof. Dr.-Ing. Timm Faulwasser
MSc course | written exam | 6 LP

In this course, you will learn the fundamentals of building models from data. The content of the course is as follows:

• Linear regression
• Subspace identification
• Nonlinear regression
• Parameter estimation
• Neural networks

The course is complemented by exercise sessions that bring theory to practice. 

If you are interested, please register and we will see you in the lecture hall!

Lecturer: Prof. Dr.-Ing. Timm Faulwasser
MSc course | oral exam | 6 LP

This course introduces distributed control and networked systems. It covers cyber-physical systems, key application domains, and motivating examples of interconnected dynamical systems. The course then presents fundamentals of algebraic graph theory, including directed graphs, matrix representations, and basic analysis tools. Building on this, it studies consensus in multi-agent systems, including control design and convergence analysis. In addition, the course addresses synchronisation in coupled systems in both linear and nonlinear settings, with examples such as Kuramoto oscillators and power-swing equations.

Content

• Introduction to distributed control and networked systems
• Algebraic graph theory
• Consensus in multi-agent control
• Synchronization of linear and nonlinear oscillators

Lecturer: Prof. Dr.-Ing. Timm Faulwasser
MSc course | written report and demonstration | 6 LP

The control labs offer a chance for students to implement familiar theoretical concepts to real systems and gain unique insight into practical applications of advanced control theory, through a series of structured experiments.

Each experiment reinforces core control concepts and introduces programming tools and technical know-how which are invaluable in industry or academia. Each experiment offers 1 ECTS. Students may earn a minimum of 2 credits and a maximum of 6 credits from by combining 2 of the three courses.

Organization

• The lab consists of 6 experiments, each worth 1 ECTS (maximum: 6 ECTS).
• Students must complete at least 2 experiments to receive credit.
• For administrative purposes, credits are grouped into three options: Lab A (4 ECTS), Lab B (3 ECTS), Lab C (2 ECTS).
• Options can be combined to match the total earned credits (up to 6 ECTS), e.g. A + C = 6 ECTS, B + C = 5 ECTS.

Stud.IP Links

• Control Lab A
• Control Lab B
• Control Lab C

Block course in the Summer Term 2025

Lecturer: Prof. Dr.-Ing. Timm Faulwasser