Search result: Catalogue data in Spring Semester 2018

Mechanical Engineering Master Information
Core Courses
Robotics, Systems and Control
The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor.
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NumberTitleTypeECTSHoursLecturers
151-0310-00LModel Predictive Engine Control Restricted registration - show details
Number of participants limited to 50.
W4 credits2V + 1UT. Albin Rajasingham
AbstractFor efficient and stable operation of an internal combustion engine a multitude of complex control tasks have to be handled. In this lecture the application of model predictive control for these control challenges is introduced.
Objective- Learn how to design and implement model predictive control algorithms for the example system “combustion engine”. Get to know the entire process from simulation-based control development to the application at a real-world combustion engine.
- Deepen the knowledge concerning the necessary control algorithms for a combustion engine.
Content- Physical phenomena and models for processes of the combustion engine such as air path and fuel path
- Analysis of the control tasks arising in engine systems
- Case studies for the application of model predictive control for combustion engines with the goal to handle the complex, multivariable system dynamics
- Fundamentals of the implementation of model predictive control
Lecture notesLecture slides will be provided after each lecture.
LiteratureL. Guzzella / C. Onder: "Introduction to Modeling and Control of Internal Combustion Engine Systems", J. Maciejowski: "Predictive Control with Constraints"
Prerequisites / NoticeEngine Systems (recommended).
151-0314-00LInformation Technologies in the Digital ProductW4 credits3GE. Zwicker, R. Montau
AbstractObjective, Methods, Concepts of the Digital Product and Product-Life-Cycle-Management (PLM)
Digital Product Fundamental: Productstructuring, Optimisation of Development- and Engineering Processes, Distribution and Use of Product Data in Sales, Production & Assembly, Service
PLM Fundamentals: Objects, Structures, Processes, Integrations
Application and Best Practices
ObjectiveThe students learn the basics and concepts of the product life cycle management (PLM), the usage of databanks, the integration of CAx-Systems, the configuration of computer networks and their protocols, moderne computer based communication (CSCW) or the variants and configuration management in regard to the creation, administration and usage of digital products.
ContentMöglichkeiten und Potentiale der Nutzung moderner IT-Tools, insbesondere moderner CAx- und PLM- Technologien. Der zielgerichtete Einsatz von CAx- und PLM-Technologien im Zusammenhang Produkt-Plattform - Unternehmensprozesse - IT-Tools. Einführung in die Konzepte des Produkt-Lifecycle-Managements (PLM): Informationsmodellierung, Verwaltung, Revisionierung, Kontrolle und Verteilung von Produktdaten bzw. Produkt-Plattformen. Detaillierter Aufbau und Funktionsweise von PLM-Systemen. Integration neuer IT-Technologien in bestehende und neu zu strukturierende Unternehmensprozesse. Möglichkeiten der Publikation und der automatischen Konfiguration von Produktvarianten auf dem Internet. Einsatz modernster Informations- und Kommunikationstechnologien (CSCW) beim Entwickeln von Produkten durch global verteilte Entwicklungszentren. Schnittstellen der rechnerintegrierten und unternehmensübergreifenden Produktentwicklung. Auswahl und Projektierung, Anpassung und Einführung von PLM-Systemen. Beispiele und Fallstudien für den industriellen Einsatz moderner Informationstechnologien.

Lehrmodule
- Einführung in die PLM-Technologie
- Datenbanktechnologie im Digitalen Produkt
- Objektmanagement
- Objektklassifikation
- Objektidentifikation mit Sachnummernsystem
- Prozess- Kooperationsmanagement
- Workflow Management
- Schnittstellen im Digitalen Produkt
- Enterprises Application Integration
Lecture notesDidaktisches Konzept/ Unterlagen/ Kosten
Die Durchführung der Lehrveranstaltung erfolgt gemischt mit Vorlesungs- und Übungsanteilen anhand von Praxisbeispielen.
Handouts für Inhalt und Case; zT. E-learning; Kosten Fr.20.--
Prerequisites / NoticeVoraussetzungen
Empfohlen:
Informatik II; Fokus-Projekt; Freude an Informationstechnologien

Testat/ Kredit-Bedingungen / Prüfung
Erfolgreiche Durchführung von Übungen in Teams
Mündliche Prüfung 30 Minuten, theoretisch und anhand konkreter Problemstellungen
151-0316-00LMethods in the Innovation Process Information Restricted registration - show details W4 credits3GC. Kobe, R. P. Haas, R.‑D. Moryson
AbstractDuring this lecture student teams have to generate and develop product innovation ideas within a given innovation fields. The lectures will give an introduction to several innovation methods and support the students to apply them.
Objective- advanced knowledge about the innovation-process
- overview on useful methods for the early innovation process
- experience in applying these methods
- capability to classify a Project situation and choose, adapt and apply appropriate methods
ContentModules (may differ from year to year):
- Innovation process
- Use cases
- Scenario techniques
- Creativity methods
- Innovation strategy
- Failure mode and effect analysis FMEA
- Quality function deployment QFD
- Target costing TC
- Decision methods
- Moderation technique
Lecture notesslides will be distributed via moodle
151-0318-00LEcodesign - Environmental-Oriented Product DevelopmentW4 credits3GR. Züst
AbstractEcodesign has a great potential to improve the environmental performance of a product.
Main topics of the lecture: Motivation for Ecodesign; Methodical basics (defining environmental aspects; improvement strageies and measures); Ecodesign implementation (systematic guidance on integrating environmental considerations into product development) in a small project.
ObjectiveExperience shows that a significant part of the environmental impact of a business venture is caused by its own products in the pre and post-production areas. The goal of eco design is to reduce the total effect of a product on the environment in all phases of product life. The systematic derivation of promising improvement measures at the start of the product development process is a key skill that will be taught in the lectures.
The participants will discover the economic and ecological potential of ECODESIGN and acquire competence in determining goal-oriented and promising improvements and will be able to apply the knowledge acquired on practical examples.
ContentDie Vorlesung ist in drei Blöcke unterteilt. Hier sollen die jeweiligen Fragen beantwortet werden:
A) Motivation und Einstieg ins Thema: Welche Material- und Energieflüsse werden durch Produkte über alle Lebensphasen, d.h. von der Rohstoffgewinnung, Herstellung, Distribution, Nutzung und Entsorgungen verursacht? Welchen Einfluss hat die Produktentwicklung auf diese Auswirkungen?
B) Grundlagen zum ECODESIGN PILOT: Wie können systematisch – über alle Produktlebensphasen hinweg betrachtet – bereits zu Beginn der Produktentwicklung bedeutende Umweltauswirkungen erkannt werden? Wie können zielgerichtet diejenigen Ecodesign-Maßnahmen ermittelt werden, die das größte ökonomische und ökologische Verbesserungspotential beinhalten?
C) Anwendung des ECODESIGN PILOT: Welche Produktlebensphasen bewirken den größten Ressourcenverbrauch? Welche Verbesserungsmöglichkeiten bewirken einen möglichst großen ökonomischen und ökologischen Nutzen?
Im Rahmen der Vorlesung werden verschiedene Praktische Beispiel bearbeitet.
Lecture notesFür den Einstieg ins Thema ECODESIGN wurde verschiedene Lehrunterlagen entwickelt, die im Kurs zur Verfügung stehen und teilwesie auch ein "distance learning" ermöglichen:

Lehrbuch: Wimmer W., Züst R.: ECODESIGN PILOT, Produkt-Innovations-, Lern- und Optimierungs-Tool für umweltgerechte Produktgestaltung mit deutsch/englischer CD-ROM; Zürich, Verlag Industrielle Organisation, 2001. ISBN 3-85743-707-3

CD: im Lehrbuch inbegriffen (oder Teil "Anwenden" on-line via: Link)
Internet: Link vermittelt verschiedene weitere Zugänge zum Thema. Zudem werden CD's abgegeben, auf denen weitere Lehrmodule vorhanden sind.
LiteratureHinweise auf Literaturen werden on-line zur Verfügung gestellt.
Prerequisites / NoticeTestatbedingungen: Abgabe von zwei Übungen
151-0530-00LNonlinear Dynamics and Chaos II Information W4 credits4GG. Haller
AbstractThe internal structure of chaos; Hamiltonian dynamical systems; Normally hyperbolic invariant manifolds; Geometric singular perturbation theory; Finite-time dynamical systems
ObjectiveThe course introduces the student to advanced, comtemporary concepts of nonlinear dynamical systems analysis.
ContentI. The internal structure of chaos: symbolic dynamics, Bernoulli shift map, sub-shifts of finite type; chaos is numerical iterations.

II.Hamiltonian dynamical systems: conservation and recurrence, stability of fixed points, integrable systems, invariant tori, Liouville-Arnold-Jost Theorem, KAM theory.

III. Normally hyperbolic invariant manifolds: Crash course on differentiable manifolds, existence, persistence, and smoothness, applications.
IV. Geometric singular perturbation theory: slow manifolds and their stability, physical examples. V. Finite-time dynamical system; detecting Invariant manifolds and coherent structures in finite-time flows
Lecture notesStudents have to prepare their own lecture notes
LiteratureBooks will be recommended in class
Prerequisites / NoticeNonlinear Dynamics I (151-0532-00) or equivalent
151-0534-00LAdvanced DynamicsW4 credits3V + 1UP. Tiso
AbstractLagrangian dynamics - Principle of virtual work and virtual power - holonomic and non holonomic contraints - 3D rigid body dynamics - equilibrium - linearization - stability - vibrations - frequency response
ObjectiveThis course provides the students of mechanical engineering with fundamental analytical mechanics for the study of complex mechanical systems .We introduce the powerful techniques of principle of virtual work and virtual power to systematically write the equation of motion of arbitrary systems subjected to holonomic and non-holonomic constraints. The linearisation around equilibrium states is then presented, together with the concept of linearised stability. Linearized models allow the study of small amplitude vibrations for unforced and forced systems. For this, we introduce the concept of vibration modes and frequencies, modal superposition and modal truncation. The case of the vibration of light damped systems is discussed. The kinematics and dynamics of 3D rigid bodies is also extensively treated.
Lecture notesLecture notes are produced in class and are downloadable right after each lecture.
LiteratureThe students will prepare their own notes. A copy of the lecture notes will be available.
Prerequisites / NoticeMechanics III or equivalent; Analysis I-II, or equivalent; Linear Algebra I-II, or equivalent.
151-0566-00LRecursive Estimation Information W4 credits2V + 1UR. D'Andrea
AbstractEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
ObjectiveLearn the basic recursive estimation methods and their underlying principles.
ContentIntroduction 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.
Lecture notesLecture notes available on course website: Link
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
151-0623-00LETH Zurich Distinguished Seminar in Robotics, Systems and Controls Information
Students for other Master's programmes in Department Mechanical and Process Engineering cannot use the credit in the category Core Courses
W1 credit1SB. Nelson, M. Chli, R. Gassert, M. Hutter, W. Karlen, R. Riener, R. Siegwart
AbstractThis course consists of a series of seven lectures given by researchers who have distinguished themselves in the area of Robotics, Systems, and Controls.
ObjectiveObtain an overview of various topics in Robotics, Systems, and Controls from leaders in the field. Please see Link for a list of upcoming lectures.
ContentThis course consists of a series of seven lectures given by researchers who have distinguished themselves in the area of Robotics, Systems, and Controls. MSc students in Robotics, Systems, and Controls are required to attend every lecture. Attendance will be monitored. If for some reason a student cannot attend one of the lectures, the student must select another ETH or University of Zurich seminar related to the field and submit a one page description of the seminar topic. Please see Link for a suggestion of other lectures.
Prerequisites / NoticeStudents are required to attend all seven lectures to obtain credit. If a student must miss a lecture then attendance at a related special lecture will be accepted that is reported in a one page summary of the attended lecture. No exceptions to this rule are allowed.
151-0630-00LNanorobotics Information W4 credits2V + 1US. Pané Vidal
AbstractNanorobotics is an interdisciplinary field that includes topics from nanotechnology and robotics. The aim of this course is to expose students to the fundamental and essential aspects of this emerging field.
ObjectiveThe aim of this course is to expose students to the fundamental and essential aspects of this emerging field. These topics include basic principles of nanorobotics, building parts for nanorobotic systems, powering and locomotion of nanorobots, manipulation, assembly and sensing using nanorobots, molecular motors, and nanorobotics for nanomedicine.
151-0634-00LPerception and Learning for Robotics
Number of participants limited to: 30

To apply for the course please create a CV in pdf of max. 2 pages, including your machine learning and/or robotics experience. Please send the pdf to Link for approval.
W4 credits1AC. D. Cadena Lerma, I. Gilitschenski, R. Siegwart
AbstractThis course covers tools from statistics and machine learning enabling the participants to deploy these algorithms as building blocks for perception pipelines on robotic tasks. All mathematical methods provided within the course will be discussed in context of and motivated by example applications mostly from robotics. The main focus of this course are student projects on robotics.
ObjectiveApplying Machine Learning methods for solving real-world robotics problems.
ContentDeep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping
Lecture notesSlides will be made available to the students.
LiteratureWill be announced in the first lecture.
Prerequisites / NoticeThe students are expected to be familiar with material of the "Recursive Estimation" and the "Learning and Intelligent Systems" lectures. Particularly understanding of basic machine learning concepts, stochastic gradient descent for neural networks, reinforcement learning basics, and knowledge of Bayesian Filtering are required. Furtheremore, good knowledge of programming in C++ and Python is required.
151-0641-00LIntroduction to Robotics and Mechatronics Information Restricted registration - show details
Number of participants limited to 60.

Enrollment is only valid through registration on the MSRL Website (Link). Registration per e-mail is no longer accepted!
W4 credits2V + 2UB. Nelson, N. Shamsudhin
AbstractThe aim of this lecture is to expose students to the fundamentals of mechatronic and robotic 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.
ObjectiveThe aim of this lecture is to expose students to the fundamentals of mechatronic and robotic 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.
ContentAn 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.
Prerequisites / NoticeThe 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.
151-0660-00LModel Predictive Control Information W4 credits2V + 1UM. Zeilinger
AbstractModel predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics.
ObjectiveDesign and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.
Content- Review of required optimal control theory
- Basics on optimization
- Receding-horizon control (MPC) for constrained linear systems
- Theoretical properties of MPC: Constraint satisfaction and stability
- Computation: Explicit and online MPC
- Practical issues: Tracking and offset-free control of constrained systems, soft constraints
- Robust MPC: Robust constraint satisfaction
- Nonlinear MPC: Theory and computation
- Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization
- Simulation-based project providing practical experience with MPC
Lecture notesScript / lecture notes will be provided.
Prerequisites / NoticeOne semester course on automatic control, Matlab, linear algebra.
Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control.

Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week).
151-0854-00LAutonomous Mobile Robots Information W5 credits4GR. Siegwart, M. Chli, J. Nieto
AbstractThe 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.
ObjectiveThe 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.
Lecture notesThis 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.
LiteratureThis 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
151-1115-00LAircraft Aerodynamics and Flight MechanicsW4 credits3GJ. Wildi
AbstractEquations of motion. Aircraft flight perfomance, flight envelope. Aircraft static stability and control, longituadinal and lateral stbility. Dynamic longitudinal and lateral stability.
Flight test. Wind tunnel test.
Objective- Knowledge of methods to solve flight mechanic problems
- To be able to apply basic methods for flight performence calculation and stability investigations
- Basic knowledge of flight and wind tunnel tests and test evaluation methods
ContentEquations of motion. Aircraft flight perfomance, flight envelope. Aircraft static stability and control, longituadinal and lateral stbility. Dynamic longitudinal and lateral stability.
Flight testing. Wind tunnel testing.
Lecture notesAusgewählte Kapitel der Flugtechnik (J. Wildi)
LiteratureMc Cormick, B.W.: Aerodynamics, Aeronautics and Flight Mechanics (John Wiley and Sons), 1979 / 1995

Anderson, J: Fundamentals of Aerodynamics (McGraw-Hill Comp Inc), 2010
Prerequisites / NoticeRecommended: Lecture 'Basics of Aircraft und Vehicle Aerodynamics' (FS)
151-0116-10LHigh Performance Computing for Science and Engineering (HPCSE) for Engineers II Information W4 credits4GP. Koumoutsakos, P. Chatzidoukas
AbstractThis course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures.
ObjectiveThe course will teach
- programming models and tools for multi and many-core architectures
- fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences
ContentHigh Performance Computing:
- Advanced topics in shared-memory programming
- Advanced topics in MPI
- GPU architectures and CUDA programming

Uncertainty Quantification:
- Uncertainty quantification under parametric and non-parametric modeling uncertainty
- Bayesian inference with model class assessment
- Markov Chain Monte Carlo simulation
Lecture notesLink
Class notes, handouts
Literature- Class notes
- Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein
- CUDA by example, J. Sanders and E. Kandrot
- Data Analysis: A Bayesian Tutorial, Devinderjit Sivia
227-0124-00LEmbedded Systems Information W6 credits4GL. Thiele
AbstractAn embedded system is some combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. The course covers theoretical and practical aspects of embedded system design and includes a series of lab sessions.
ObjectiveUnderstanding specific requirements and problems arising in embedded system applications.

Understanding architectures and components, their hardware-software interfaces, the memory architecture, communication between components, embedded operating systems, real-time scheduling theory, shared resources, low-power and low-energy design as well as hardware architecture synthesis.

Using the formal models and methods in embedded system design in practical applications using the programming language C, the operating system FreeRTOS, a commercial embedded system platform and the associated design environment.
ContentAn embedded system is some combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. For example, they are part of industrial machines, agricultural and process industry devices, automobiles, medical equipment, cameras, household appliances, airplanes, sensor networks, internet-of-things, as well as mobile devices.

The focus of this lecture is on the design of embedded systems using formal models and methods as well as computer-based synthesis methods. Besides, the lecture is complemented by laboratory sessions where students learn to program in C, to base their design on the embedded operating systems FreeRTOS, to use a commercial embedded system platform including sensors, and to edit/debug via an integrated development environment.

Specifically the following topics will be covered in the course: Embedded system architectures and components, hardware-software interfaces and memory architecture, software design methodology, communication, embedded operating systems, real-time scheduling, shared resources, low-power and low-energy design, hardware architecture synthesis.

More information is available at Link .
Lecture notesThe following information will be available: Lecture material, publications, exercise sheets and laboratory documentation at Link .
LiteratureP. Marwedel: Embedded System Design, Springer, ISBN 978-3-319-56045-8, 2018.

G.C. Buttazzo: Hard Real-Time Computing Systems. Springer Verlag, ISBN 978-1-4614-0676-1, 2011.

Edward A. Lee and Sanjit A. Seshia: Introduction to Embedded Systems, A Cyber-Physical Systems Approach, Second Edition, MIT Press, ISBN 978-0-262-53381-2, 2017.

M. Wolf: Computers as Components – Principles of Embedded System Design. Morgan Kaufman Publishers, ISBN 978-0-128-05387-4, 2016.
Prerequisites / NoticePrerequisites: Basic knowledge in computer architectures and programming.
227-0216-00LControl Systems II Information W6 credits4GR. Smith
AbstractIntroduction to basic and advanced concepts of modern feedback control.
ObjectiveIntroduction to basic and advanced concepts of modern feedback control.
ContentThis 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.
Lecture notesThe slides of the lecture are available to download.
LiteratureSkogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Prerequisites / NoticePrerequisites:
Control Systems or equivalent
227-0224-00LStochastic Systems Information W4 credits2V + 1UF. Herzog
AbstractProbability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
ObjectiveStochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance.
Content- Stochastic processes
- Stochastic calculus (Ito)
- Stochastic differential equations
- Discrete time stochastic difference equations
- Stochastic processes AR, MA, ARMA, ARMAX, GARCH
- Kalman filter
- Stochastic optimal control
- Applications in finance and engineering
Lecture notesH. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
227-0690-09LAdvanced Topics in Control (Spring 2018) Information
New topics are introduced every year.
W4 credits2V + 2UF. Dörfler
AbstractThis 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 2018 the class will concentrate on distributed systems and control.
ObjectiveThe 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 2018 the class will be taught by F. Dörfler and will focus on distributed systems and control.
ContentDistributed 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.
Lecture notesA set of self-contained set of lecture notes will be made available.
LiteratureRelevant papers and books will be made available through the course website.
Prerequisites / NoticeControl systems (227-0216-00L), Linear system theory (227-0225-00L), or equivalents, as well as sufficient mathematical maturity.
252-0526-00LStatistical Learning Theory Information W6 credits2V + 3PJ. M. Buhmann
AbstractThe course covers advanced methods of statistical learning :
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.
ObjectiveThe 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.
Content# 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.

# 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.

# Statistical physics models: approaches for large systems approximate optimization, which originate in the statistical physics (free energy minimization applied to spin glasses and other models); sampling methods based on these models
Lecture notesA draft of a script will be provided;
transparencies of the lectures will be made available.
LiteratureHastie, 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
Prerequisites / NoticeRequirements:

knowledge of the Machine Learning course
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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