Courses

The faculty of the International Max Planck Research School for Intelligent Systems offer numerous courses that may appeal to our Scholars and Associated Students. With few exceptions, the courses listed here take place at the Univeristy of Tuebingen, the University of Stuttgart, or one of the two sites of the Max Planck Institute for Intelligent Systems. These courses typically require in-person attendance.

Click the course name to be taken to the more detailed course listing below:

Advanced Artificial Neural Networks

Advanced ANN topics. First, revisiting backprogation and backpropagation through time; then: Advanced Recurrent Neural Networks (LSTM); Deep Learning; Convolution; Reservoir Computing; Dynamic NNs; Hierarchical Vision Architectures; Restricted Boltzmann Machines; Predictive Encoding & Free Energy; Gain Fields and Switching Networks; Generative Networks.

Location: University of Tuebingen, Germany
Lecturer: Martin Butz
Duration: 1 Semester
Semester: winter semester
Contact: Martin Butz

Advanced Artificial Neural Networks (practical course)

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

Location: University of Tuebingen, Germany
Lecturer: Martin Butz
Duration: 1 Semester
Semester: irregular
Contact: Martin Butz

Analysis and Control of Multiagent Systems

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.

Location: University of Stuttgart, Germany
Lecturer: Daniel Zelazo
Duration: 3 h lectures + 1 h exercises (6)
Contact: Frank Allgöwer

Atome, Moleküle und ihre Spektroskopie (Lehramt)

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.

Location: University of Stuttgart, Germany
Lecturer: as of WS 18/19 Peer Fischer
Duration: twice a week tbd
Semester: Ws 18/19, annually
Contact: Peer Fischer

Avatars in Virtual Realities (practical course)

Programming enhanced functionalities in ANNs (including convolutional ANNs, RNNs, ESNs, etc.), evaluating performance, analyzing the system.

Location: University of Tuebingen, Germany
Lecturer: Martin Butz
Duration: 1 Semester
Semester: winter semester
Contact: Martin Butz

Behavior and Learning

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.

Location: University of Tuebingen, Germany
Lecturer: Martin Butz
Duration: 1 Semester
Semester: summer semester
Contact: Martin Butz

Cognitive Modeling

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.

Location: University of Tuebingen, Germany
Lecturer: Martin Butz and Felix Wichmann
Duration: 1 Semester
Semester: winter semester
Contact: Martin Butz

Computational Motor Control

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.

Location: University of Tuebingen, Germany
Lecturer: Daniel Häufle and Winfried Ilg
Duration: 1.5 hours/week
Semester: Spring/Summer
Contact: Daniel Häufle

Computational Photography

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.

Location: University of Tuebingen, Germany
Lecturer: Hendrik Lensch, Jieen Chen, Raphael Braun
Duration: 2h lectures + 2 h exercises
Semester: Yearly

Dynamik nichtglatter Systeme (lecture given in German)

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).

Location: University of Stuttgart, Germany
Lecturer: Viktor Avrutin
Duration: 2 h lectures (3)
Semester: summer
Contact: Frank Allgöwer

Einführung in die Chaostheorie (lecture given in German)

  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

Location: University of Stuttgart, Germany
Lecturer: Viktor Avrutin
Duration: 3 h lectures + 1 h exercises (6)
Semester: winter
Contact: Frank Allgöwer

Einführung in die Chemie (lecture given in German)

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

Location: University of Stuttgart, Germany
Lecturer: Peer Fischer, Clemens Richert, Thomas Schleid,
Duration: 1 Semester
Semester: WS17/18, WS 18/19, annually
Contact: Peer Fischer

Einführung in die Regelungstechnik (lecture given in German)

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

Location: University of Stuttgart, Germany
Lecturer: Frank Allgöwer
Duration: 2 hrs lectures (3)
Semester: winter
Contact: Frank Allgöwer

Ethik und Wissenschaftstheorie des Maschinellen Lernens (lecture given in English)

„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.

Location: University of Tuebingen, Germany
Lecturer: Ulrike von Luxburg
Duration: 2 SWS
Semester: WS 17/18
Contact: Ulrike von Luxburg

Introduction to Adaptive Control

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.

Location: University of Stuttgart, Germany
Lecturer: Dieter Schwarzmann
Duration: 2 h lectures (3)
Semester: winter
Contact: Frank Allgöwer

Introduction to Machine Learning

Linear regression, Features, Regularization, Cross validation, Lasso

Classification & Structured Output, Logistic regression, Structured Output & Structured Input, Conditional Random Fields

Kernelization & Structured Input, Other loss functions, Support Vector Machines, Neural Networks, Unsupervised learning, Kernel PCA, Clustering, Embedding, Local learning, Ensembles of weak and randomized learners, Boosting, Bayesian Regression & Classification, Bayesian Kernel Ridge Regression, Gaussian Processes

Location: University of Stuttgart, Germany
Lecturer: Marc Toussaint
Duration: 2+2
Semester: summer

Introduction to Psychtoolbox

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.

Location: University of Tuebingen, Germany
Lecturer: Heiko Schütt
Duration: 2 SWS lecture and practical session
Semester: Irregular

Introduction to Robotics

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

Location: University of Stuttgart, Germany
Lecturer: Marc Toussaint
Duration: 2+2
Semester: winter
Contact: Marc Toussaint

Konzepte der Regelungstechnik (lecture given in German)

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

Location: University of Stuttgart, Germany
Lecturer: Frank Allgöwer
Duration: 3 h lectures + 1 h exercises (6)
Semester: winter
Contact: Frank Allgöwer

Machine Learning Algorithms and Theory

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.

Location: University of Tuebingen, Germany
Lecturer: Ulrike von Luxburg
Semester: summer term 18
Contact: Ulrike von Luxburg

Massively Parallel Computing

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

Location: University of Tuebingen, Germany
Lecturer: Benjamin Resch, Fabian Groh, Hendrik Lensch
Duration: 5 days block course
Semester: 01.03.-07.03 (Yearly)

Maths for Intelligent systems

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

Location: University of Stuttgart, Germany
Lecturer: Marc Toussaint
Duration: 2+2
Semester: winter

Medical Data Science

- 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)

Location: University of Tuebingen, Germany
Lecturer: Nico Pfeifer
Duration: Weekly (6 ECTS)
Semester: Summer Semester

Mehrgrößenregelung (lecture given in German)

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, ...)

Location: University of Stuttgart, Germany
Lecturer: Frank Allgöwer
Duration: 1.5 h lectures + 0.5 h exercises (3)
Semester: summer
Contact: Frank Allgöwer

Model Predictive Control

Semester: summer
Contact: Frank Allgöwer

Neural Data Analysis

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.

Location: University of Tuebingen, Germany
Lecturer: Berens/Ecker
Duration: 2+2 SWS
Semester: Summer
Contact: Philipp Berens

Nonlinear Control

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

    Lecturer: Frank Allgöwer
    Duration: 3 h lectures + 1 h exercises (6
    Contact: Frank Allgöwer

    Optimal Control

    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

    Location: University of Stuttgart, Germany
    Lecturer: Christian Ebenbauer
    Duration: 3 h lectures + 1 h exercises (6)
    Semester: winter
    Contact: Frank Allgöwer

    Psychophysics and Non-invasive Methods

    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.

    Location: University of Tuebingen, Germany
    Lecturer: Felix Wichmann
    Duration: 30h/2SWS
    Semester: anually in summer term

    Rendering

    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.

    Location: University of Tuebingen, Germany
    Lecturer: Hendrik Lensch, Raphael Braun, Sebastian Herholz
    Duration: 2h lectures + 2 h exercises
    Semester: Yearly
    Contact: Hendrik Lensch

    Seminar Motion in Man and Machine

    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.

    Location: University of Tuebingen, Germany
    Lecturer: Daniel Häufle
    Duration: 3 days
    Semester: Spring/Summer
    Contact: Daniel Häufle

    Sensory Psychology

    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.

    Location: University of Tuebingen, Germany
    Lecturer: Felix Wichmann
    Duration: If students take the module, then: VL Psychophysical Methods is compulsory S Spatial Vision is optional S Colour Vision & Material Perception is optional Thus the lecture and one of the seminars has to be taken. 2 SWS lecture and 2 SWS seminar
    Semester: Lecture Psychophysical Methods yearly every SoSe Seminar Spatial Vision bi-annually in the SoSe Seminar Colour Vision & Material Perception bi-annually in the SoSe

    Statistische Lernverfahren und Stochastische Regelung (lecture given in German)

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

    Location: University of Stuttgart, Germany
    Lecturer: Christian Ebenbauer, Nicole Radde, Sebastian Trimpe
    Duration: 2 h lectures + 2 h exercises (6)
    Semester: winter
    Contact: Frank Allgöwer

    Surfaces & Colloids (35710)

    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.

    Location: University of Stuttgart, Germany
    Lecturer: Peer Fischer, Cosima Stubenrauch, Thomas Sottmann,
    Duration: 1 Semester
    Semester: every second year WS 17/18,WS 19/20
    Contact: Peer Fischer

    Theoretical and Methodological Foundations of Autonomous Systems

    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

    Location: University of Stuttgart, Germany
    Lecturer: Jim Mainprice
    Duration: 10 lectures, 10 tutorials
    Semester: Master's winter term 2017/18
    Contact: Heiko Zimmermann

    Theory of Machine Learning

    In this seminar we discuss current research papers and results in the area of learning theory. Most sessions take place in form of a reading group: everybody reads the assigned paper before the meeting. Then we are going to discuss the paper in the meeting. Sometimes we also have talks by guests or members of the group. Everybody is welcome, but it is on voluntary basis (you cannot get any credit points for this seminar). It might sense to attend the seminar if you consider to write your Master thesis in the TML group.

    Location: University of Tuebingen, Germany
    Lecturer: Ulrike von Luxburg
    Semester: WS 17/18 and summer term 18
    Contact: Ulrike von Luxburg

    Vorlesung Angewandte Statistik II (Lecture Course Applied Statistics II)

    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.

    Location: University of Tuebingen, Germany
    Lecturer: Uli Wannek
    Duration: 60 h / 4 SWS
    Semester: alle 2 Jahre im Sommersemester

    Vorlesung Optik, Lichtvermessung, Displaytechnik (Lecture Course Optics, Light Measurement, Display Technology)

    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.

    Location: University of Tuebingen, Germany
    Lecturer: Uli Wannek
    Duration: 60 h / 4 SWS
    Semester: 2-jährig im Sommersemester