The first lecture will take place on Wednesday, November 04 2026, 2:00 pm - 3:30 pm (Neuer Chemie Hörsaal). In the winter term 2026/2027, the OC lecture will be offered in a hybrid format, i. e. in presence in the lecture hall and a complimentary live stream via Zoom.
Zoom-Link: https://kit-lecture.zoom-x.de/j/66774432160
ILIAS-Link: https://ilias.studium.kit.edu/goto.php/crs/3057153
The password will be announced in the first lecture.

Contact

If you have any questions concerning the lecture or the exercise, please contact Jan Riffel.

Recommendations

Systemdynamik und Regelungstechnik
Signals and Systems
Advanced Mathematics I-III

Teaching Content

Learn the mathematical principles behind modern intelligent systems:
 

• Learn methods that power Reinforcement Learning and Artificial Intelligence.
• Understand Bellman Principle, the backbone of RL algorithms.
• Apply optimization techniques to real-world problems in robotics and autonomous systems.
   

Course Highlights:
 

• Dynamic Programming → Basis for RL algorithms
• Calculus of Variations → Used in trajectory optimization
• Model Predictive Control → Applied in autonomous vehicles and robots
• Unconstrained Parameter Optimization → Powers machine learning model training
• Constrained Parameter Optimization → Applied in robotics for safe motion planning

Literature

J. Nocedal: Numerical Optimization
M. Papageorgiou: Optimierung
K. Donald: Optimal Control Theory - An Introduction
A. E. Bryson: Applied Optimal Control

Workload

Presence in Lecture and Exercise: 60 h

Preperation and revision of the course content: 120 h

Goals

The students
 

• know as well the mathematical basics as the fundamental methods and algorithms to solve constrained and unconstrained nonlinear static optimization problems.
• can solve constraint and unconstraint dynamic optimization by using the calculus of variations approach and the Dynamic Programming method.
• understand and can apply the Bellman Principle, including its theoretical foundations and practical relevance.
• are able to transfer dynamic optimization problems to static problems.
• know the mathematic relations, the pros and cons and the limits of each optimization method.
• can transfer problems from other fields of their studies in a suitable optimization problem formulation and they are able to select and implement appropriate optimization algorithms for them by using common software tools

exam  WS26/27 on Wednesday, 10th March 2027 
 

Results & Review

The results and the review date will be announced on the home page. The results as well as date and location of the review will be published in the showcase of the IRS (ground floor, building 11.20).

WS24/25 lecture
WS24/25 exercise