Courses

Programming and design of intelligent, realistic, interesting, behaving avatars, objects, and tools in virtual realities.

Die Studierenden

• verstehen die quantenmechanischen Grundlagen der Spektroskopie, sowie die Grundlagen der Elektrochemie,
• beherrschen grundlegende spektroskopische und elektrochemische Methoden in Theorie und Praxis und
• können diese zur Lösung chemierelevanter Fragestellungen anwenden.

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

„Intelligente Technologien” verändern die Welt. Sie dringen in unterschiedlichste Bereiche von Technik, Industrie und Wirtschaft vor und haben das Potential, unsere Gesellschaft zu verändern. Grundlage dafür bilden Techniken aus dem Bereich des maschinellen Lernens, die es Algorithmen erlauben, immer komplexere Aufgaben zu erfüllen und selbständig Entscheidungen zu treffen. Als InformatikerIn sieht man sich dadurch mit neuen ethischen Fragestellungen konfrontiert, die bisher so nicht vorgekommen sind, und die sich nur schwer ignorieren lassen. Ziel dieses Seminars ist, Studierende aus verschiedenen Fachrichtungen zusammenzubringen, um gemeinsam über solche Fragen zu diskutieren: Wie sollen Algorithmen Entscheidungen treffen / nicht treffen? Wer trägt die Verantwortung dafür, wenn etwas schief läuft? Wie können / sollen wir verhindern, dass Algorithmen bestimmte Gesellschaftsgruppen diskriminieren (ohne dass es explizit gewollt ist)? Was ist ”faires’’ maschinelles Lernen (gibt es das überhaupt)? Inwiefern können und sollen Entscheidungen von Algorithmen transparent und nachvollziehbar sein? Wird sich die Gesellschaft durch maschinelles Lernen verändern, sollen/wollen/können wir das steuern?

Ein zweites Themengebiet dieses Seminars ist die Wissenschaftstheorie: wie wird sich die wissenschaftliche Herangehensweise verändern, wenn Algorithmen des maschinellen Lernens auf einmal eine zentrale Rolle im wissenschaftlichen Erkenntnisprozess innehaben? Welche Art von Erkenntnissen kann man durch eine algorithmisch getriebene Wissenschaft erzielen, welche nicht?

Ziel des Seminars ist nicht, alle diese Fragen zu beantworten, sondern sie zu diskutieren, und sowohl Studierenden der Informatik , den Kognitionswissenschaften als auch der Philosophie/ Ethik exemplarisch demonstrieren, wie solche Fragen angegangen werden können.

The Machine Learning course first covers basic regression and classification methods (e.g. Bayesian Kernel Ridge Logistic Regression...) and then focuses on Bayesian formulations of learning (Bayes nets, probabilistic inference). In Stuttgart I plan to iterate the course every summer.

Lectures are weekly, Thursdays, 14:00. Tutorials are weekly on Monday.

Kinematics, Inverse Kinematics, Singularity, operational space, motion profiles, trajectory interpolation, Multiple tasks

Dynamics, Reference Oscillator, PID, Euler-Lagrange equation, Newton-Euler recursion, Inverse and forward dynamics, Trajectory tracking, Operational space control

Path Planning, trajectory optimization, Probabilistic Road Maps, RRTs, Non-holonomic systems

Mobile Robotics, Simultaneous Localization and Mapping (SLAM)

Control Theory, Hamilton-Jacobi-Bellman equation, LQ, Riccati, Controllability, Lyapunov and exponential stability,

Reinforcement Learning in Robotics

In many applications and domains, massive amounts of data are collected and processed every day. To be able to make efficient use of such data, there is an urgent need for tools to extract important pieces of information from the flood of unimportant details.

Machine learning is a relatively young discipline that tries to deal with this problem, by designing algorithms to analyze large amounts of complex data in a principled way. Machine learning is the core technique in many applications such as spam filtering, object recognition, analyzing user preferences, recommender systems, and so on. Scientific disciplines such as biology, neuroscience, physics, or medicine discover the potential of machine learning methods for analyzing their empirical data. And, last but not least, many large companies like google, Amazon, facebook heavily rely on machine learning techniques.

The field of machine learning combines ingredients from several fields: we need to design efficient algorithms to process the amount of data, and we need to ensure that predictions made by machine learning algorithms are statistically sound.

The focus of the lecture is on algorithmic and theoretical aspects of machine learning. We will cover many of the standard algorithms, learn about the general principles for building good machine learning algorithms, and analyze their theoretical properties.

- Supervised learning problems: Linear methods; regularization; SVMS; kernel methods - Unsupervised learning problems: Dimension reduction (kernel PCA, multi-dimensional scaling, manifold methods); spectral clustering and spectral graph theory - How to model machine learning problems: Bayesian decision theory, loss functions, feature selection, evaluation and comparison of algorithms. Common pitfalls - Online algorithms - Learning theory (no free lunch theorem; generalization bounds; VC dimension; universal consistency; Theorem of Stone)

- Low rank matrix methods (collaborative filtering, low rank matrix completion, compressed sensing)

The following topics are NOT going to be covered: decision trees, neural networks / deep networks, graphical models, Bayesian approaches to machine learning, reinforcement learning.

Linear Algebra, Vectors, dual vectors, coordinates, matrices, tensors, Coordinate free, The Singular Value Decomposition Theorem, Eigendecomposition, Power Method

Derivatives, Coordinate free, total/partial, chain rules & autodiff ”Gradient vectors”, Co- and contra-variance, Taylor expansion

Optimization, KKT conditions, Log Barrier, Augmented Lagrangian, The Lagrangian, unconstrained optimization, Backtracking line search, Wolfe conditions, & convergence, The Newton direction, Gauss-Newton, Quasi-Newton & BFGS, Conjugate Gradient, Blackbox & Global Optimization, Global Optimization as infinite bandidts

Probabilities & Information, Bayes, Conjugate distributions, neg-log-probabilities & exp-neg-energies, Information, Entropie & Kullback-Leibler, The Laplace approximation, Variational Inference, The Fisher information metric, Maximum Entropy and Maximum Likelihood, Learning = Compression

Analyse und Synthese mehrschleifiger linearer Regelkreise im Zeit- und Frequenzbereich.

• Analyse von Mehrgrößen-systemen (Singulärwerte-Diagramme, Relative Gain Array (RGA), ...)
• Reglerentwurf für Mehrgrößensysteme im Frequenzbereich (Verallg. Nyquist-Kriterium, Direct Nyquist Array Verfahren (DNA),...)
• Reglerentwurf für Mehrgrößensysteme im Zeitbereich (Steuerungs-invarianz, Störentkopplung, ...)

In recent years our experimental methods to record brain activity have been revolutionized. As the complexity of the data acquired by neurophysiologists increases, neural data analysis becomes ever more important: The complex multidimensional signals recorded with multielectrode arrays or two-photon imaging can no longer be interpreted by eye, but mathematical and statistical techniques are needed.

In this practical course we will cover a selection of topics related to the analysis of different kinds of neural data: basic descriptive and inferential statistics, time series analysis, spike triggered average/covariance, spike sorting, dimensionality reduction techniques and information theory. The focus will be on hands-on experience in data analysis.

In many practical control problems it is desired to optimize a given cost functional while satisfying constraints involving dynamical systems. These kind of problems typically fall into the area of optimal control, a centerpiece of modern control theory. This course gives an introduction to the theory and application of optimal control for linear and nonlinear systems. Topics covered in the course are: Nonlinear programming approach Dynamic programming Model predictive control Pontryagin maximum principle Applications

This is an advanced course on photo realistic image synthesis. The course will cover the theory of global illumination computations and will give an introduction to Monte Carlo and Quasi-Monte Carlo methods as one way of solving the rendering equation. In addition, we will discuss surface appearance models, and their representation based on spherical harmonics or wavelets, leading to other photo realistic rendering techniques such as precomputed radiance transfer methods.The goal is to achieve real-time global illumination.

Lecture Psychophysical Methods

• Linear systems theory, psychophysical methods and experimental design, signal detection theory, diffusion models for reaction times, psychometric function estimation.

Seminar Spatial Vision

• Optics of the eye, absolute thresholds, adaptation, contrast sensitivity function, spatial frequency selectivity, contrast gain-control, early visual representation of the world.

Seminar Colour Vision & Material Perception

• Spectral composition of light, wavelength encoding, colour matching, trichromacy, colour appearance, colour constancy, material properties & perception.

Diese Lehrveranstaltung gibt eine Einführung in folgende Themengebiete. • Stochastische Simulation und Samplingverfahren • Bayessche Schätzverfahren und Filter • Gaußsche Prozesse und Regression

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

Aufbauend auf Angewandte Statistik I werden komplexere statistische Methoden behandelt: Generalisierte Lineare Modelle (GLM), Hauptkomponentenanalyse (PCA), Unabhängigkeitsanalyse (ICA) und Bayes-Statistik. Der Schwerpunkt liegt auf der praktischen Anwendung aller Methoden und deren Implementation in der Programmiersprache Python (mit den Modulen statsmodels, scipy.stats, sklearn und pystan) und der Darstellung der Ergebnisse in Notebooks. Die Studenten sollen weiterführende statistische Methoden kennen-, anwenden und in Software implementieren lernen. Die Unterschiede zwischen frequentistischer und Bayes-Statistik werden hinterfragt. Angeeignetes Wissen und Erfahrung soll die Studenten in die Lage versetzen, Versuche selbst planen und auswerten zu können und dabei typische Fehler zu vermeiden. In der Literatur dargestellte Ergebnisse werden kritisch hinterfragt.