Courses

Multi-agent systems are systems composed of multiple interacting dynamic units. These units can be used to perform team objectives with applications ranging from formation flying to distributed computation. Challenges associated with these systems are their analysis and synthesis, arising due to their decoupled, distributed, large-scale nature, and due to limited inter-agent sensing/communication capabilities. This course provides an introduction to these systems via tools from graph theory and dynamic systems theory. The course will also cover real-world applications by presenting recent results obtained in the distributed formation control of real multi-robot systems.

Based on our knowledge about how animals and humans plan their behavior, make behavioral decisions, control their behavior, and progressively optimize and adapt it, behavioral decision making, control, optimization, and adaptation algorithms are introduced. In particular, the lecture introduces spatial representations for behavioral control, forward-inverse control models, including the learning of such representations and models. Also the encoding and the learning of motor control primitives and motor complexes is considered. Last but not least, self-motivated artificial systems are considered that strive to maintain internal homeostasis and to maximize information gain.

In our daily live, we are able to perform complex movements and to adapt our movement behavior continuously to changing environments. For doing so, our motor control system performs continuously various control and adaptation processes on different controls levels. In order to be able to understand and to model these control and adaptation processes, basic knowledge on the interaction between neural control and biomechanics is necessary. Goals of this lecture are on the one hand to get an understanding of motor behavior as an interaction of neural control and biomechanics and (changing) environments and on the other hand to learn methods for the development of models of motor control and motor learning processes.

Non-smooth dynamical systems are in the meanwhile well accepted as a mathematical tool which provides an adequate description for many applications originating from such areas, as electronics (any kinds of switching circuits) and mechanics (impacting and friction dominated systems) as well as social sciences (systems including decision making) and economics (business cycles models). The goal of this lecture course is to present a broad overview of possible effects both from the theoretical and from the applied perspectives. We consider the classes of attractors and other invariant sets which appear in such systems, as well as their possible bifurcations (border collision bifurcations, degenerate bifurcations, homoclinic bifurcations leading to transformations of chaotic attractors).

(Vorlesungsverzeichnis Nr. 05007) für Studierende der folgenden Fachrichtungen: Chemie-Bachelor, Chemie-Höheres Lehramt, Lebensmittelchemie, Chemie-Bachelor of Arts, Materialwissenschaft, Mathematik und Technikpädagogik

Adaptive Control deals with the control of systems with unknown parameters. This is done by learning the controller parameters online and ensuring a stable operation of the closed-loop system. An adaptive controller, essentially, is a nonlinear controller that turns into a linear controller (for linear systems) once learning has finished.

This lecture aims at introducing the theory and practice, abilities and limitations of the mainstream adaptive control concept, namely "Direct Model-Reference Adaptive Control" in continuous-time. This method is widely used e.g. in flight control of modern aircrafts. We will talk about other approaches and adaptive control of nonlinear systems, but focus on the understanding of the above, in theory and practice.

The learning goals are how and why an adaptive controller works, when to use it, when not, how to improve it, and some robustness extensions.

Learn how to achieve the following with psychtoolbox

Drawing primitives, images, and textures Use alpha-mapping for blending and masking Play and loop sounds Accurate concurrent button-press detection Structuring experiments for reproducibility and robustness

By reflecting upon the practical considerations that go into the implementation of psychological experiments, students will acquire a grasp of the different paradigms, technicalities and nuances of writing an experiment using Psychtoolbox.

Aufbauend auf den Inhalten von Einführung in die Regelungstechnik werden in dieser Vorlesung Konzepte aus folgenden Bereichen behandelt • Regelung nichtlinearer Systeme • Optimale Regelung • Robuste Regelung

Graphics processors contain hundreds of parallel processing elements and thus enable us to explore this realm of massively parallel computing today. The high number of parallel cores poses a great challenge for software design that must expose massive parallelism to benefit from the new hardware. The main purpose of the lecture is to teach practical algorithm design for such parallel hardware.

Introduction to Parallel Computing Basic Algorithms: - Map, reduce, parallel branching, sorting - Parallel data storage and retrieval Parallel Computation: - FFT, particle systems - Parallel linear equation solvers, parallel PDEs - Parallel complexity analysis and profiling - System integration and graphics processor clusters

- This lecture comprises different areas of Medical Data Science. Data Science or statistical machine learning methods have the potential to transform personal health care over the coming years. Advances in the technologies have generated large biological data sets. In order to gain insights that can then be used to improve preventive care or treatment of patients, these big data have to be stored in a way that enables fast querying of relevant characteristics of the data and consequently building statistical models that represent the dependencies between variables. These models can then be utilized to derive new biomedical principals, provide evidence for or against certain hypotheses, and to assist medical professionals in their decision process. Specific topics are: - Gaining new insights from medical data - Modeling uncertainty in medical data science models -Making medical findings available through interpretable decision support systems - Method-wise, the lecture will introduce methods for GWAS analyses (e.g., LMMs), methods for sequence analysis (e.g., kernel methods), methods for “small n problems” (e.g., domain adaptation, transfer learning, and multitask learning), methods for data integration (advanced unsupervised learning methods), methods for learning probabilistic Machine Learning models (e.g., graphical models), methods for large data sets (e.g., deep learning models)

The course covers modern analysis and controller design methods for nonlinear systems:

1. Differential Equations
1. Existence of Solutions
2. Lyapunov's Direct Method
3. Uniqueness of Solutions
2. Nonautonomous Differential Equations
1. Lyapunov's Direct Method
2. Comparison Functions
3. Systems with Inputs
1. Input-to-State Stability
2. Control Lyapunov Functions
3. Backstepping
4. Systems with Inputs and Outputs
1. Sliding Mode Control
2. Dissipativity
3. Passivity
5. Input-Output Methods
1. Signals and Systems
2. Feedback Theorems

The Vision Sciences are an interdisciplinary field, with researchers having diverse backgrounds from psychology, biology, or medicine to physics, computer science and engineering. Analysing and designing experiments in the Vision Sciences thus requires knowledge straddling the typical boundaries of many disciplines. In this course we will cover some physics (light), electrical engineering (display devices), mathematical psychology (signal detection theory) and statistics (psychometric function estimation) in sufficient detail, to allow the students to both analyse and critically assess psychophysical experiments in the literature, as well as to design their own psychophysical experiments.

Participants will acquire the necessary knowledge to critically assess experiments in the vision sciences as well as the necessary skills to design and analyse their own behavioural (psychophysical) experiments. Through homework assignments and computer exercises they will gain hands-on experience applying signal detection theory and psychometric function estimation to data, and avoid common pitfalls.

Short description of the course: This seminar offers a unique possibility to glance at research questions, methods, and results in three research labs in Baden-Württemberg. The guiding question for the seminar is: How are biological and technical movements generated and controlled? In the seminar, you will get insights into this interdisciplinary field. We will give an overview including aspects like recording and analysis of human movement, reduced biomechanical models, neuro-musculo-skeletal models, humanoid robotics, control of complex human-like movements, and machine learning.

Content - Loss functions and surrogate loss functions - Regularized empirical risk minimization - Ingredients from empirical process theory - Reproducing kernel Hilbert spaces - Kernel-based learning machines

Level: M.Sc. course in mathematics

Beginning this week, lectures are on: Monday 11:45 to 13:15, Friday 9:45 to 11:15

Beginning next week, the tutorials are on: Friday 14:00 to 15:30

This course provides a rigorous introduction to stochastic calculus and stochastic processes. Anyone who is interested can participate.

The course will recap essentials of linear algebra, optimization, probabilities, and statistics in order to equip students with the basics of speaking maths to formulate problems in intelligent systems research

Wann immer in der Informatik Informationen visualisiert werden, kommen Bildschirme & Beamer zum Einsatz: In der Computergraphik, der Medizininformatik oder der Mensch-Computer-Interaktion. Bildschirme & Beamer werden auch bei visueller Stimulation in Experimenten der Kognitions- und den Neurowissenschaften verwendet, und für präzise Stimuluspräsentation müssen diese ausgemessen und kalibriert werden. Themen: Einführung in die Physik des Lichts und die Photometrie; Technik von Versuchsmonitoren (LCD,CRT, LED); Technik von optischen Messgeräten (Photometer, Spektrometer, Lichtmesskamera, Transientenrekorder); Vermessung und Optimieren der Darstellung; Programmieren präziser Experimente mit speziellen Toolboxen (Psychopy, Psychtoolbox). Studenten lernen die technischen Grundlagen von Bildschirmen & Beamern kennen. Das erworbene Verständnis versetzt die Studenten in die Lage, Vor- und Nachteile sowie Grenzen gängiger Display Technologien beurteilen und messen zu können. Am Ende der Vorlesung mit praktischen Übungen sind die Studierenden in der Lage, selbständig eigene visuelle Experimente in der Kognitionswissenschaft und der Medienwissenschaft durchzuführen und typische Fehler bei der Kalibrierung und Nutzung von Displays zu vermeiden.