Suchergebnis: Katalogdaten im Frühjahrssemester 2015

Elektrotechnik und Informationstechnologie Master Information
Fächer der Vertiefung
Insgesamt 42 KP müssen im Masterstudium aus Vertiefungsfächern erreicht werden. Der individuelle Studienplan unterliegt der Zustimmung eines Tutors.
Systems and Control
Kernfächer
Diese Fächer sind besonders Empfohlen, um sich in "Systems and Control" zu vertiefen.
NummerTitelTypECTSUmfangDozierende
151-0566-00LRecursive Estimation Information W4 KP2V + 1UR. D'Andrea
KurzbeschreibungEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
LernzielLearn the basic recursive estimation methods and their underlying principles.
InhaltIntroduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
SkriptLecture notes available on course website: Link
Voraussetzungen / BesonderesRequirements: Introductory probability theory and matrix-vector algebra.
227-0207-00LNonlinear Systems and Control Information
Voraussetzung: Control Systems (227-0103-00L)
W6 KP4GE. Gallestey Alvarez, P. F. Al Hokayem
KurzbeschreibungVermittlung von den Grundlagen für die Modellierung und Analyse von Nichtlineare Systeme,sowie eine Übersicht der verschiedene nichtlinearen Reglerentwurfsmethoden.
LernzielDie Studenten kennen die unterschiede zwischen lineare und nichtlineare Systeme, die Mathematische Grundlagen für deren Modellierung und Analyse, und kene auch die verschiedene Möglichkeiten, einen Regler für das nichtlineares System zu entwerfen.
InhaltFast alle in der Praxis auftretenden Regelprobleme zeichnen sich durch einen mehr oder weniger ausgeprägten nichtlinearen Charakter aus. In manchen Fällen genügt die Anwendung linearer Regelverfahren. In vielen anderen Fällen kann befriedigendes Regelverhalten lediglich durch Einsatz nichtlinearer Methoden erreicht werden. In den vergangenen Jahrzehnten sind auf dem Gebiet der nichtlinearen Regelung ausgereifte Methoden zur Bearbeitung praktischer nichtlinearer Regelungsprobleme entwickelt worden.
Diese Vorlesung versteht sich als Einführung in das Gebiet der nichtlinearen Systemen und Regelung. Es werden keine Grundkenntnisse in nichtlinearer Regelung vorausgesetzt. Es wird aber angenommen, dass die Hörer mit Grundkonzepten der linearen Regelung vertraut sind, wie sie zum Beispiel im Kernfach "Regelsysteme" vermittelt werden.
SkriptEin Skript in englischer Sprache wird während der Vorlesung auf dem Homepage zur Verfügung gestellt.
LiteraturH.K. Khalil: Nonlinear Systems, Prentice Hall, 2001.
Voraussetzungen / BesonderesVoraussetzungen: Regelsysteme oder äquivalente Vorlesung.
227-0216-00LControl Systems II Information W6 KP4GR. Smith
KurzbeschreibungIntroduction to basic and advanced concepts of modern feedback control.
LernzielIntroduction to basic and advanced concepts of modern feedback control.
InhaltThis course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues.
SkriptThe slides of the lecture are available to download
LiteraturSkogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Voraussetzungen / BesonderesPrerequisites:
Control Systems or equivalent
227-0221-00LModel Predictive Control Information Belegung eingeschränkt - Details anzeigen
Eintrag auf Einschreibeliste erforderlich (siehe "Besonderes").
W6 KP4GM. Morari
KurzbeschreibungSystem complexity and demanding performance render traditional control inadequate. Applications from the process industry to the communications sector increasingly use MPC. The last years saw tremendous progress in this interdisciplinary area. The course first gives an overview of basic concepts and then uses them to derive MPC algorithms. There are exercises and invited speakers from industry.
LernzielIncreased system complexity and more demanding performance requirements have rendered traditional control laws inadequate regardless if simple PID loops are considered or robust feedback controllers designed according to some H2/infinity criterion. Applications ranging from the process industries to the automotive and the communications sector are making increased use of Model Predictive Control (MPC), where a fixed control law is replaced by on-line optimization performed over a receding horizon. The advantage is that MPC can deal with almost any time-varying process and specifications, limited only by the availability of real-time computer power.
In the last few years we have seen tremendous progress in this interdisciplinary area where fundamentals of systems theory, computation and optimization interact. For example, methods have emerged to handle hybrid systems, i.e. systems comprising both continuous and discrete components. Also, it is now possible to perform most of the computations off-line thus reducing the control law to a simple look-up table.
The first part of the course is an overview of basic concepts of system theory and optimization, including hybrid systems and multi-parametric programming. In the second part we show how these concepts are utilized to derive MPC algorithms and to establish their properties. On the last day, speakers from various industries talk about a wide range of applications where MPC was used with great benefit.
There will be exercise sessions throughout the course where the students can test their understanding of the material. We will make use of the MPC Toolbox for Matlab that is distributed by MathWorks.
InhaltTentative Program

Day 1: Linear Systems I
Fundamentals of linear system theory – Review (system representations, poles, zeros, stability, controllability & observability, stochastic system descriptions, modeling of noise).

Day 2: Linear Systems II
Optimal control and filtering for linear systems (linear quadratic regulator, linear observer, Kalman Filter, separation principle, Riccati Difference Equation).

Days 3 and 4: Basics on Optimization
Fundamentals of optimization (linear programming, quadratic programming, mixed integer linear/quadratic programming, duality theory, KKT conditions, constrained optimization solvers).
Exercises.

Day 5: Introduction to MPC
MPC – concept and formulation, finite horizon optimal control, receding horizon control, stability and feasibility, computation.
Exercises.

Day 6: Numerical methods for MPC
Unconstrained Optimization, Constrained Optimization, Software applications

Day 7: Practical Aspects, Explicit & Hybrid MPC
- Reference tracking and soft constraints
- Explicit solution to MPC for linear constrained systems. Motivation. Introduction to (multi)-parametric programming through a simple example. Multi-parametric linear and quadratic programming: geometric algorithm. Formulation of MPC for linear constrained systems as a multi-parametric linear/quadratic program. A brief introduction to Multi-parametric Toolbox.
- MPC for discrete-time hybrid systems. Introduction to hybrid systems. Models of hybrid systems (MLD, DHA, PWA, etc.). Equivalence between different models. Modelling using HYSDEL. MLD systems. MPC based on MILP/MIQP. Explicit solution: mpMILP. Short introduction into dynamic programming (DP). Computation of the explicit MPC for PWA systems based on DP. Exercises.

Day 8: Applications
Invited speakers from industry and academia, different case studies

Day 9
Design exercise
SkriptScript / lecture notes will be provided.
Voraussetzungen / BesonderesPrerequisites:
One semester course on automatic control, Matlab, linear algebra.

ETH students:
As participation is limited, a reservation (e-mail: Link) is required. Please give information on your "Studienrichtung", semester, institute, etc.
After your reservation has been confirmed, please register online at Link.

Interested persons from outside ETH:
It is not possible/needed to enrol as external auditor for this course. Please contact Alain Bolle to register for the course (Link).

We have only a limited number of places in the course, it is "first come, first served"!
227-0224-00LStochastic Systems Information W4 KP2V + 1UF. Herzog
KurzbeschreibungWahrscheinlichkeit. Zufallsprozesse. Stochastische Differentialgleichungen. Stochastische Differenz Gleichungen, Ito. Kalman-Filter. Stochastische optimale Regelung. Anwendungen in Finanz-Problemen.
LernzielBeschreibung, Filterung und Optimierung von dynamischen stochastischen Systemen. Anwendungsgebiete aus Technik und Finanzmathematik werden anhand von Beispielen präsentiert.
Inhalt- Stochastische Prozesse
- Stochastische Differentialrechnung
- Stochastische Differentialgleichungen
- Diskrete stochastische Differenzengleichungen
- Stochastische Prozesse AR, MA, ARMA, ARMAX, GARCH
- Kalman Filter
- Stochastische optimale Regelung (diskret und kontinuierlich)
- Anwendungen auf dem Gebiet der Finanzmathematik und Technik
SkriptH. P. Geering u. a., Stochastic Systems, Institut für Mess- und Regeltechnik, 2007 und Unterlagen
227-0690-06LAdvanced Topics in Control (Spring 2015) Information
New topics are introduced every year.
W4 KP2V + 2UF. Dörfler
KurzbeschreibungThis class will introduce students to advanced, research level topics in the area of automatic control. Coverage varies from semester to semester, repetition for credit is possible, upon consent of the instructor. During the Spring Semester 2015 the class will concentrate on distributed systems and control.
LernzielThe intent is to introduce students to advanced research level topics in the area of automatic control. The course is jointly organized by Prof. R. D'Andrea, L. Guzzella, J. Lygeros, M. Morari, R. Smith, and F. Dörfler. Coverage and instructor varies from semester to semester. Repetition for credit is possible, upon consent of the instructor. During the Spring Semester 2015 the class will be taught by F. Dörfler and will focus on distributed systems and control.
InhaltDistributed control systems include large-scale physical systems, engineered multi-agent systems, as well as their interconnection in cyber-physical systems. Representative examples are the electric power grid, camera networks, and robotic sensor networks. The challenges associated with these systems arise due to their coupled, distributed, and large-scale nature, and due to limited sensing, communication, and control capabilities. This course covers modeling, analysis, and design of distributed control systems.

Topics covered in the course include:
- the theory of graphs (with an emphasis on algebraic and spectral graph theory);
- basic models of multi-agent and interconnected dynamical systems;
- continuous-time and discrete-time distributed averaging algorithms (consensus);
- coordination algorithms for rendezvous, formation, flocking, and deployment;
- applications in robotic coordination, coupled oscillators, social networks, sensor networks, electric power grids, epidemics, and positive systems.
SkriptA set of self-contained set of lecture nodes will be made available on the course website.
LiteraturRelevant papers and books will be made available through the course website.
Voraussetzungen / BesonderesControl systems (227-0216-00L), Linear system theory (227-0225-00L), or equivalents, as well as sufficient mathematical maturity.
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NummerTitelTypECTSUmfangDozierende
376-1217-00LRehabilitation Engineering I: Motor Functions Information W3 KP2V + 1UR. Riener
Kurzbeschreibung“Rehabilitation engineering” is the application of science and technology to ameliorate the handicaps of individuals with disabilities in order to reintegrate them into society. The goal of this lecture is to present classical and new rehabilitation engineering principles and examples applied to compensate or enhance especially motor deficits.
LernzielProvide theoretical and practical knowledge of principles and applications used to rehabilitate individuals with motor disabilities.
Inhalt“Rehabilitation” is the (re)integration of an individual with a disability into society. Rehabilitation engineering is “the application of science and technology to ameliorate the handicaps of individuals with disability”. Such handicaps can be classified into motor, sensor, and cognitive (also communicational) disabilities. In general, one can distinguish orthotic and prosthetic methods to overcome these disabilities. Orthoses support existing but affected body functions (e.g., glasses, crutches), while prostheses compensate for lost body functions (e.g., cochlea implant, artificial limbs). In case of sensory disorders, the lost function can also be substituted by other modalities (e.g. tactile Braille display for vision impaired persons).

The goal of this lecture is to present classical and new technical principles as well as specific examples applied to compensate or enhance mainly motor deficits. Modern methods rely more and more on the application of multi-modal and interactive techniques. Multi-modal means that visual, acoustical, tactile, and kinaesthetic sensor channels are exploited by displaying the patient with a maximum amount of information in order to compensate his/her impairment. Interaction means that the exchange of information and energy occurs bi-directionally between the rehabilitation device and the human being. Thus, the device cooperates with the patient rather than imposing an inflexible strategy (e.g., movement) upon the patient. Multi-modality and interactivity have the potential to increase the therapeutical outcome compared to classical rehabilitation strategies.
In the 1 h exercise the students will learn how to solve representative problems with computational methods applied to exoprosthetics, wheelchair dynamics, rehabilitation robotics and neuroprosthetics.
SkriptLecture notes will be distributed at the beginning of the lecture (1st session)
LiteraturIntroductory Books

Neural prostheses - replacing motor function after desease or disability. Eds.: R. Stein, H. Peckham, D. Popovic. New York and Oxford: Oxford University Press.

Advances in Rehabilitation Robotics – Human-Friendly Technologies on Movement Assistance and Restoration for People with Disabilities. Eds: Z.Z. Bien, D. Stefanov (Lecture Notes in Control and Information Science, No. 306). Springer Verlag Berlin 2004.

Intelligent Systems and Technologies in Rehabilitation Engineering. Eds: H.N.L. Teodorescu, L.C. Jain (International Series on Computational Intelligence). CRC Press Boca Raton, 2001.

Control of Movement for the Physically Disabled. Eds.: D. Popovic, T. Sinkjaer. Springer Verlag London, 2000.

Interaktive und autonome Systeme der Medizintechnik - Funktionswiederherstellung und Organersatz. Herausgeber: J. Werner, Oldenbourg Wissenschaftsverlag 2005.

Biomechanics and Neural Control of Posture and Movement. Eds.: J.M. Winters, P.E. Crago. Springer New York, 2000.

Selected Journal Articles

Abbas, J., Riener, R. (2001) Using mathematical models and advanced control systems techniques to enhance neuroprosthesis function. Neuromodulation 4, pp. 187-195.

Burdea, G., Popescu, V., Hentz, V., and Colbert, K. (2000): Virtual reality-based orthopedic telerehabilitation, IEEE Trans. Rehab. Eng., 8, pp. 430-432

Colombo, G., Jörg, M., Schreier, R., Dietz, V. (2000) Treadmill training of paraplegic patients using a robotic orthosis. Journal of Rehabilitation Research and Development, vol. 37, pp. 693-700.

Colombo, G., Jörg, M., Jezernik, S. (2002) Automatisiertes Lokomotionstraining auf dem Laufband. Automatisierungstechnik at, vol. 50, pp. 287-295.

Cooper, R. (1993) Stability of a wheelchair controlled by a human. IEEE Transactions on Rehabilitation Engineering 1, pp. 193-206.

Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T. (1998): Robot-aided neurorehabilitation, IEEE Trans. Rehab. Eng., 6, pp. 75-87

Leifer, L. (1981): Rehabilitive robotics, Robot Age, pp. 4-11

Platz, T. (2003): Evidenzbasierte Armrehabilitation: Eine systematische Literaturübersicht, Nervenarzt, 74, pp. 841-849

Quintern, J. (1998) Application of functional electrical stimulation in paraplegic patients. NeuroRehabilitation 10, pp. 205-250.

Riener, R., Nef, T., Colombo, G. (2005) Robot-aided neurorehabilitation for the upper extremities. Medical & Biological Engineering & Computing 43(1), pp. 2-10.

Riener, R., Fuhr, T., Schneider, J. (2002) On the complexity of biomechanical models used for neuroprosthesis development. International Journal of Mechanics in Medicine and Biology 2, pp. 389-404.

Riener, R. (1999) Model-based development of neuroprostheses for paraplegic patients. Royal Philosophical Transactions: Biological Sciences 354, pp. 877-894.
Voraussetzungen / BesonderesTarget Group:
Students of higher semesters and PhD students of
- D-MAVT, D-ITET, D-INFK
- Biomedical Engineering
- Medical Faculty, University of Zurich
Students of other departments, faculties, courses are also welcome
151-0104-00LUncertainty Quantification for Engineering & Life Sciences Belegung eingeschränkt - Details anzeigen
Findet dieses Semester nicht statt.
Number of participants limited to 40.
W4 KP3GP. Koumoutsakos
KurzbeschreibungQuantification of uncertainties in computational models pertaining to applications in engineering and life sciences. Exploitation of massively available data to develop computational models with quantifiable predictive capabilities. Applications of Uncertainty Quantification and Propagation to problems in mechanics, control, systems and cell biology.
LernzielThe course will teach fundamental concept of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences. Emphasis will be placed on practical and computational aspects of UQ+P including the implementation of relevant algorithms in multicore architectures.
InhaltTopics that will be covered include: Uncertainty quantification under
parametric and non-parametric modelling uncertainty, Bayesian inference with model class assessment, Markov Chain Monte Carlo simulation, prior and posterior reliability analysis.
SkriptThe class will be largely based on the book: Data Analysis: A Bayesian Tutorial by Devinderjit Sivia as well as on class notes and related literature that will be distributed in class.
Literatur1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia
2. Probability Theory: The Logic of Science by E. T. Jaynes
3. Class Notes
Voraussetzungen / BesonderesFundamentals of Probability, Fundamentals of Computational Modeling
151-0532-00LNonlinear Dynamics and Chaos I Information W4 KP2V + 1UD. Karrasch, G. Haller
KurzbeschreibungBasic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.
LernzielThis course is intended for Masters and Ph.D. students in engineering sciences, physics and applied mathematics who are interested in the behavior of nonlinear dynamical systems. It offers an introduction to the qualitative study of nonlinear physical phenomena modeled by differential equations or discrete maps. We discuss applications in classical mechanics, electrical engineering, fluid mechanics, and biology. A more advanced Part II of this class is offered every other year.
Inhalt(1) Basic facts about nonlinear systems: Existence, uniqueness, and dependence on initial data.

(2) Near equilibrium dynamics: Linear and Lyapunov stability

(3) Bifurcations of equilibria: Center manifolds, normal forms, and elementary bifurcations

(4) Nonlinear dynamical systems on the plane: Phase plane techniques, limit sets, and limit cycles.

(5) Time-dependent dynamical systems: Floquet theory, Poincare maps, averaging methods, resonance
SkriptThe class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture.
Voraussetzungen / Besonderes- Prerequisites: Analysis, linear algebra and a basic course in differential equations.

- Exam: two-hour written exam in English.

- Homework: A homework assignment will be due roughly every other week. Hints to solutions will be posted after the homework due dates.
151-0641-00LIntroduction to Robotics and Mechatronics Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 60. COURSE IS FULLY BOOKED!

The enrollment is only valid if an e-mail is sent to Link with "IRM participation" in the subject. Enrollment is valid starting from September 2014. The order of enrollment will be considered according to the time your e-mail is sent.
W4 KP2V + 2UB. Nelson
KurzbeschreibungThe aim of this lecture is to expose students to the fundamentals of these systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use.
LernzielThe aim of this lecture is to expose students to the fundamentals of these systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use, and forward and inverse kinematics. Throughout the course students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems.
InhaltAn ever increasing number of mechatronic systems are finding their way into our daily lives. Mechatronic systems synergistically combine computer science, electrical engineering, and mechanical engineering. Robotics systems can be viewed as a subset of mechatronics that focuses on sophisticated control of moving devices. The aim of this lecture is to expose students to the fundamentals of these systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use, and forward and inverse kinematics. Throughout the course students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems.
Voraussetzungen / BesonderesThe registration is limited to 60 students.
There are 4 credit points for this lecture.
The lecture will be held in English.
The students are expected to be familiar with C programming.
151-0854-00LAutonomous Mobile Robots Information W5 KP4GP. Furgale, M. Hutter, M. Rufli, D. Scaramuzza, R. Siegwart
KurzbeschreibungThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.
LernzielThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation.
SkriptThis lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.
LiteraturThis lecture is based on the Textbook:
Introduction to Autonomous Mobile Robots
Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
227-0529-00LSmartGrids: System Optimization of Smart and Liberalized Electric Power Systems Information W6 KP4GR. Bacher
KurzbeschreibungModel based optimization of SmartGrids systems considering Physics, Economics and Legislation; Optimality conditions and solutions; Lagrange-Multipliers and market prices; Price incentives in case of restrictions and grid constraints; Transmission grid congestions and implicit auctions; Security of supply with high variability + market requirements; Electricity market and SmartGrids system models.
Lernziel- Understanding the legal, physical and market based framework for Smart Grid based electric power systems.
- Understanding the theory of mathematical optimization models and algorithms for a secure and market based operation of Smart Power Systems.
- Gaining experience with the formulation, implementation and computation of constrained optimization problems for Smart Grid and market based electricity systems.
Inhalt- Legal conditions for the regulation and operation of electric power systems (CH, EU).
- Physical laws and constraints in electric power systems.
- Special characteristics of the good "electricity".
- Optimization as mathematical tool for analyzing network based electric power systems.
- Types of optimization problems, optimality conditions and optimization methods.
- Various electricity market models, their advantages and disadvantages.
- SmartGrids: The new energy system and compatibility issues with traditional market models.
SkriptText book is continuously updated and distributed to students.
LiteraturClass text book contains active hyperlinks related to back ground material.
Voraussetzungen / BesonderesMotivation, Active participation (discussions). Numerical analysis, power system basics and modeling, optimization basics
252-0526-00LStatistical Learning Theory Information W4 KP2V + 1UJ. M. Buhmann
KurzbeschreibungThe course covers advanced methods of statistical learning :
PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models.
LernzielThe course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed.
Inhalt# Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification.
# Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come.
# Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error?
# Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include:

* Maximum Entropy
* Information Bottleneck
* Deterministic Annealing

# Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures.
# Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike.
# Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future.
Skriptno script; transparencies of the lectures will be made available.
LiteraturDuda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000.

Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001.

L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
Voraussetzungen / BesonderesRequirements:

basic knowledge of statistics, interest in statistical methods.

It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Statistical Learning Theory can be followed without the introductory course.
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