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.

Cognitive models covering learning, action and perception are presented and discussed, including descriptive, qualitative, quantitative and neural models. In addition, parameter optimization as well as techniques to compare models and to interpret and evaluate model parameters are introduced. All techniques are shown in the context of concrete models of cognitive processes.

Although the digital photography industry is expanding rapidly, most digital cameras still look and feel like film cameras, and they offer roughly the same set of features and controls. However, as sensors and in-camera processing systems improve, these cameras will begin to offer capabilities that film cameras never had. Among these will be the ability to refocus photographs after they are taken, or to combine views taken with different camera settings, aim, or placement. Equally exciting are new technologies for creating efficient, controllable illumination. Future "flashbulbs" may be pulsed LEDs or video projectors, with the ability to selectively illuminate objects, recolor the scene, or extract shape information. These developments force us to relax our notion of what constitutes "a photograph." They also blur the distinction between photography and scene modeling. These changes will lead to new photographic techniques, new scientific tools, and possibly new art forms.

In this course we will survey the converging technologies of digital photography, computational imaging, and image-based rendering, and we will explore the new imaging modalities that they enable.

1. Problemstellungen und Grundbegriffe
2. Qualitative Analyse: Attraktoren (periodische, aperiodische,chaotische Trajektorien), Bifurkationen (lokale und globale Bifurkationen, Bifurkationen in stückweise glatten Systemen); Bifurkationsszenarien (in glatten und stückweise glatten Systemen)
3. Quantitative Analyse: Lyapunov-Exponenten, fraktale Dimensionen, weitere Maße. Symbolische Dynamik
4. Fraktale

Reglerentwurf für lineare zeitinvariante Systeme im Zeit- und Frequenz-bereich. Insbesondere:

• Anforderungen an einen Regelkreis
• Reglerentwurf mittels loop-shaping
• Polvorgabe und Beobachterentwurf

Erweiterte Regelkreis-strukturen (Störgrößen-aufschaltung, Kaskaden-regelung, 2-DOF Design, Anti-Windup

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 students are able to

• apply the fundamentals of physical chemistry when describing characteristics of surfaces and colloids.
• describe the significance of structure-property relationships on different length scales (macro, micro, nano)
• identify characteristic properties of surfactant solutions and microemulsions by employing appropriate experimental techniques and methods.
• interpret experimental results properly and submit adequate written reports on those results.
• give coherent oral reports on complex scientific problems in the field of surfaces and colloids.

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.