Suchergebnis: Katalogdaten im Herbstsemester 2016

Rechnergestützte Wissenschaften Master Information
Vertiefungsgebiete
Systems and Control
NummerTitelTypECTSUmfangDozierende
151-0575-01LSignals and Systems Information W4 KP2V + 2UR. D'Andrea
KurzbeschreibungSignals arise in most engineering applications. They contain information about the behavior of physical systems. Systems respond to signals and produce other signals. In this course, we explore how signals can be represented and manipulated, and their effects on systems. We further explore how we can discover basic system properties by exciting a system with various types of signals.
LernzielMaster the basics of signals and systems. Apply this knowledge to problems in the homework assignments and programming exercise.
InhaltDiscrete-time signals and systems. Fourier- and z-Transforms. Frequency domain characterization of signals and systems. System identification. Time series analysis. Filter design.
SkriptLecture notes available on course website.
151-0563-01LDynamic Programming and Optimal Control Information W4 KP2V + 1UR. D'Andrea
KurzbeschreibungIntroduction to Dynamic Programming and Optimal Control.
LernzielCovers the fundamental concepts of Dynamic Programming & Optimal Control.
InhaltDynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.
LiteraturDynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.
Voraussetzungen / BesonderesRequirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.
401-5850-00LSeminar in Systems and Control for CSEW4 KP2SJ. Lygeros
Kurzbeschreibung
Lernziel
Robotik
NummerTitelTypECTSUmfangDozierende
151-0601-00LTheory of Robotics and Mechatronics Information W4 KP3GP. Korba, S. Stoeter, B. Nelson
KurzbeschreibungThis course provides an introduction and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. It’s a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems.
LernzielRobotics is often viewed from three perspectives: perception (sensing), manipulation (affecting changes in the world), and cognition (intelligence). Robotic systems integrate aspects of all three of these areas. This course provides an introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. This course is a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems.
InhaltAn introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
Skriptavailable.
Voraussetzungen / BesonderesThe course will be taught in English.
252-0535-00LMachine Learning Information W8 KP3V + 2U + 2AJ. M. Buhmann
KurzbeschreibungMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
LernzielStudents will be familiarized with the most important concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. A machine learning project will provide an opportunity to test the machine learning algorithms on real world data.
InhaltThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

- Bayesian theory of optimal decisions
- Maximum likelihood and Bayesian parameter inference
- Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM)
- Ensemble methods: Bagging and Boosting
- Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off
- Non parametric density estimation: Parzen windows, nearest nieghbour
- Dimension reduction: principal component analysis (PCA) and beyond
SkriptNo lecture notes, but slides will be made available on the course webpage.
LiteraturC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Voraussetzungen / BesonderesThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should at least have followed one previous course offered by the Machine Learning Institute (e.g., CIL or LIS) or an equivalent course offered by another institution.
263-5902-00LComputer Vision Information W6 KP3V + 1U + 1AL. Van Gool, V. Ferrari, A. Geiger
KurzbeschreibungThe goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises.
LernzielThe objectives of this course are:
1. To introduce the fundamental problems of computer vision.
2. To introduce the main concepts and techniques used to solve those.
3. To enable participants to implement solutions for reasonably complex problems.
4. To enable participants to make sense of the computer vision literature.
InhaltCamera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition
Voraussetzungen / BesonderesIt is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course.
151-0563-01LDynamic Programming and Optimal Control Information W4 KP2V + 1UR. D'Andrea
KurzbeschreibungIntroduction to Dynamic Programming and Optimal Control.
LernzielCovers the fundamental concepts of Dynamic Programming & Optimal Control.
InhaltDynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.
LiteraturDynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.
Voraussetzungen / BesonderesRequirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.
151-0851-00LRobot Dynamics Information Belegung eingeschränkt - Details anzeigen W4 KP2V + 1UM. Hutter, R. Siegwart, T. Stastny
KurzbeschreibungWe will provide an overview on how to kinematically and dynamically model typical robotic systems such as robot arms, legged robots, rotary wing systems, or fixed wing.
LernzielThe primary objective of this course is that the student deepens an applied understanding of how to model the most common robotic systems. The student receives a solid background in kinematics, dynamics, and rotations of multi-body systems. On the basis of state of the art applications, he/she will learn all necessary tools to work in the field of design or control of robotic systems.
InhaltThe course consists of three parts: First, we will refresh and deepen the student's knowledge in kinematics, dynamics, and rotations of multi-body systems. In this context, the learning material will build upon the courses for mechanics and dynamics available at ETH, with the particular focus on their application to robotic systems. The goal is to foster the conceptual understanding of similarities and differences among the various types of robots. In the second part, we will apply the learned material to classical robotic arms as well as legged systems and discuss kinematic constraints and interaction forces. In the third part, focus is put on modeling fixed wing aircraft, along with related design and control concepts. In this context, we also touch aerodynamics and flight mechanics to an extent typically required in robotics. The last part finally covers different helicopter types, with a focus on quadrotors and the coaxial configuration which we see today in many UAV applications. Case studies on all main topics provide the link to real applications and to the state of the art in robotics.
Voraussetzungen / BesonderesThe contents of the following ETH Bachelor lectures or equivalent are assumed to be known: Mechanics and Dynamics, Control, Basics in Fluid Dynamics.
401-5860-00LSeminar in Robotics for CSE Information W4 KP2SR. Siegwart
KurzbeschreibungThis course provides an opportunity to familiarize yourself with the advanced topics of robotics and mechatronics research. The study plan has to be discussed with the lecturer based on your specific interests and/or the relevant seminar series such as the IRIS's Robotics Seminars and BiRONZ lectures, for example.
LernzielThe students are familiar with the challenges of the fascinating and interdisciplinary field of Robotics and Mechatronics. They are introduced in the basics of independent non-experimental scientific research and are able to summarize and to present the results efficiently.
InhaltThis 4 ECTS course requires each student to discuss a study plan with the lecturer and select minimum 10 relevant scientific publications to read through, or attend 5-10 lectures of the public robotics oriented seminars (e.g. Public robotics seminars such as the IRIS's Robotics Seminars Link, and BiRONZ lectures Link are good examples). At the end of semester, the results should be presented in an oral presentation and summarized in a report, which takes the discussion of the presentation into account.
Physik
Für das Vertiefungsgebiet "Physik" sind Grundkenntnisse in Quantenmechnik erforderlich.
NummerTitelTypECTSUmfangDozierende
402-0809-00LIntroduction to Computational PhysicsW8 KP2V + 2UH. J. Herrmann
KurzbeschreibungDiese Vorlesung bietet eine Einführung in Computersimulationsmethoden für physikalische Probleme und deren Implementierung auf PCs und Supercomputern: klassische Bewegungsgleichungen, partielle Differentialgleichungen (Wellengleichung, Diffussionsgleichung, Maxwell-Gleichungen), Monte Carlo Simulation, Perkolation, Phasenübergänge
Lernziel
InhaltEinführung in die rechnergestützte Simulation physikalischer Probleme. Anhand einfacher Modelle aus der klassischen Mechanik, Elektrodynamik und statistischen Mechanik sowie interdisziplinären Anwendungen werden die wichtigsten objektorientierten Programmiermethoden für numerische Simulationen (überwiegend in C++) erläutert. Daneben wird eine Einführung in die Programmierung von Vektorsupercomputern und parallelen Rechnern, sowie ein Überblick über vorhandene Softwarebibliotheken für numerische Simulationen geboten.
Voraussetzungen / BesonderesVorlesung und Uebung in Englisch, Pruefung wahlweise auf Deutsch oder Englisch
402-0205-00LQuantenmechanik I Information W10 KP3V + 2UT. K. Gehrmann
KurzbeschreibungEinführung in die nicht-relativistische Einteilchen-Quantenmechanik. Diskussion grundlegender Ideen der Quantenmechanik, insbesondere Quantisierung klassischer Systeme, Wellenfunktionen und die Beschreibung von Observablen durch Operatoren auf einem Hilbertraum, und die Analyse von Symmetrien. Grundlegende Phänomene werden analysiert und durch generische Beispiele illustriert.
LernzielEinführung in die Einteilchen Quantenmechanik. Beherrschung grundlegender Ideen (Quantisierung, Operatorformalismus, Symmetrien, Störungstheorie) und generischer Beispiele und Anwendungen (gebunden Zustände, Tunneleffekt, Streutheorie in ein- und dreidimensionalen Problemen). Fähigkeit zur Lösung einfacher Probleme.
InhaltStichworte: Schrödinger-Gleichung, Formalismus der Quantenmechanik (Zustände, Operatoren, Kommutatoren, Messprozess), Symmetrien (Translation, Rotationen), Quantenmechanik in einer Dimension, Zentralkraftprobleme, Potentialstreuung, Störungstheorie, Variations-Verfahren, Drehimpuls, Spin, Drehimpulsaddition, Relation QM und klassische Physik.
LiteraturF. Schwabl: Quantenmechanik
J.J. Sakurai: Modern Quantum Mechanics
W. Nolting: Quantenmechanik (Theoretische Physik 5.1, 5.2)
C. Cohen-Tannoudji: Quantenmechanik I
401-5810-00LSeminar in Physics for CSEW4 KP2SA. Soluyanov, M. Troyer
KurzbeschreibungIn this seminar the students present a talk on an advanced topic in modern theoretical or computational physics.
Lernziel
Computational Finance
NummerTitelTypECTSUmfangDozierende
401-3913-01LMathematical Foundations for FinanceW4 KP3V + 2UE. W. Farkas, M. Schweizer
KurzbeschreibungFirst introduction to main modelling ideas and mathematical tools from mathematical finance
LernzielThis course gives a first introduction to the main modelling ideas and mathematical tools from mathematical finance. It aims at a double audience: mathematicians who want to learn the modelling ideas and concepts for finance, and non-mathematicians who need an introduction to the main tools from stochastics used in mathematical finance. The main emphasis will be on ideas, but important results will be given with (sometimes partial) proofs.
InhaltTopics to be covered include

- financial market models in finite discrete time
- absence of arbitrage and martingale measures
- valuation and hedging in complete markets
- basics about Brownian motion
- stochastic integration
- stochastic calculus: Itô's formula, Girsanov transformation, Itô's representation theorem
- Black-Scholes formula
SkriptLecture notes will be sold at the beginning of the course.
LiteraturLecture notes will be sold at the beginning of the course. Additional (background) references are given there.
Voraussetzungen / BesonderesPrerequisites: Results and facts from probability theory as in the book "Probability Essentials" by J. Jacod and P. Protter will be used freely. Especially participants without a direct mathematics background are strongly advised to familiarise themselves with those tools before (or very quickly during) the course. (A possible alternative to the above English textbook are the (German) lecture notes for the standard course "Wahrscheinlichkeitstheorie".)

For those who are not sure about their background, we suggest to look at the exercises in Chapters 8, 9, 22-25, 28 of the Jacod/Protter book. If these pose problems, you will have a hard time during the course. So be prepared.
401-4657-00LNumerical Analysis of Stochastic Ordinary Differential Equations Information
Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods"
W6 KP3V + 1UA. Jentzen
KurzbeschreibungCourse on numerical approximations of stochastic ordinary differential equations driven by Wiener processes. These equations have several applications, for example in financial option valuation. This course also contains an introduction to random number generation and Monte Carlo methods for random variables.
LernzielThe aim of this course is to enable the students to carry out simulations and their mathematical convergence analysis for stochastic models originating from applications such as mathematical finance. For this the course teaches a decent knowledge of the different numerical methods, their underlying ideas, convergence properties and implementation issues.
InhaltGeneration of random numbers
Monte Carlo methods for the numerical integration of random variables
Stochastic processes and Brownian motion
Stochastic ordinary differential equations (SODEs)
Numerical approximations of SODEs
Multilevel Monte Carlo methods for SODEs
Applications to computational finance: Option valuation
SkriptLecture Notes are available in the lecture homepage (please follow the link in the Learning materials section).
LiteraturP. Glassermann:
Monte Carlo Methods in Financial Engineering.
Springer-Verlag, New York, 2004.

P. E. Kloeden and E. Platen:
Numerical Solution of Stochastic Differential Equations.
Springer-Verlag, Berlin, 1992.
Voraussetzungen / BesonderesPrerequisites:

Mandatory: Probability and measure theory,
basic numerical analysis and
basics of MATLAB programming.

a) mandatory courses:
Elementary Probability,
Probability Theory I.

b) recommended courses:
Stochastic Processes.

Start of lectures: Wednesday, September 21, 2016
For more details, please follow the link in the Learning materials section.
401-8905-00LFinancial Engineering (University of Zurich)
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: MFOEC103

Beachten Sie die Einschreibungstermine an der UZH: Link
W4.5 KP3GUni-Dozierende
KurzbeschreibungThis lecture is intended for students who would like to learn more on equity derivatives modelling and pricing.
LernzielQuantitative models for European option pricing (including stochastic
volatility and jump models), volatility and variance derivatives,
American and exotic options.
InhaltAfter introducing fundamental
concepts of mathematical finance including no-arbitrage, portfolio
replication and risk-neutral measure, we will present the main models
that can be used for pricing and hedging European options e.g. Black-
Scholes model, stochastic and jump-diffusion models, and highlight their
assumptions and limitations. We will cover several types of derivatives
such as European and American options, Barrier options and Variance-
Swaps. Basic knowledge in probability theory and stochastic calculus is
required. Besides attending class, we strongly encourage students to
stay informed on financial matters, especially by reading daily
financial newspapers such as the Financial Times or the Wall Street
Journal.
SkriptScript.
Voraussetzungen / BesonderesBasic knowledge of probability theory and stochastic calculus.
Asset Pricing.
401-5820-00LSeminar in Computational Finance for CSEW4 KP2SJ. Teichmann
Kurzbeschreibung
Lernziel
InhaltWe aim to comprehend recent and exciting research on the nature of
stochastic volatility: an extensive econometric research [4] lead to new in-
sights on stochastic volatility, in particular that very rough fractional pro-
cesses of Hurst index about 0.1 actually provide very attractive models. Also
from the point of view of pricing [1] and microfoundations [2] these models
are very convincing.
More precisely each student is expected to work on one specified task
consisting of a theoretical part and an implementation with financial data,
whose results should be presented in a 45 minutes presentation.
Literatur[1] C. Bayer, P. Friz, and J. Gatheral. Pricing under rough volatility.
Quantitative Finance , 16(6):887-904, 2016.

[2] F. M. Euch, Omar El and M. Rosenbaum. The microstructural founda-
tions of leverage effect and rough volatility. arXiv:1609.05177 , 2016.

[3] O. E. Euch and M. Rosenbaum. The characteristic function of rough
Heston models. arXiv:1609.02108 , 2016.

[4] J. Gatheral, T. Jaisson, and M. Rosenbaum. Volatility is rough.
arXiv:1410.3394 , 2014.
Voraussetzungen / BesonderesRequirements: sound understanding of stochastic concepts and of con-
cepts of mathematical Finance, ability to implement econometric or simula-
tion routines in MATLAB.
Electromagnetics
NummerTitelTypECTSUmfangDozierende
227-0110-00LElektromagnetische Wellen für Fortgeschrittene
Die Vorlesung wird per Studienjahr 2016/17 auf das Herbstsemester verschoben. Im FS 2017 findet sie also nicht mehr statt.
W6 KP2V + 2UP. Leuchtmann
KurzbeschreibungDie Vorlesung gibt einen vertieften Einblick in das Verhalten elektromagnetischer Wellen in linearen Materialien, inklusive negativem Brechungsindex oder Metamaterialien.
LernzielSie verstehen das Verhalten elektromagnetischer Wellen sowohl im homogenen Raum als auch in ausgewählten Strukturen (Oberflächen, geschichtete Medien, zylindrische Strukturen, Wellenleiter) und wissen auch über zeitharmonische Materialmodelle in Plasmonik Bescheid.
InhaltBeschreibung von zeitharmonischen Feldern; die Rolle des Materials in den Maxwell'schen Gleichungen; Energietransport- und -absorbierungsmechanismen; Elektromagnetische Wellen im homogenen Raum: gewöhnliche und evaneszente Ebene Wellen, Zylinderwellen, Kugelwellen, "Complex origin"-Wellen und -Strahlen; Oberflächen-Wellen; Wellen in geschichteten Strukturen; Mechanismus der Führung elektromagnetischer Wellen; TEM-Wellen; Hohlleiter und dielektrische Wellenleiter.
SkriptEin englischsprachiges Skript mit animierten Darstellungen kann heruntergeladen werden, ebenso die in der Vorlesung gezeigten Folien.
LiteraturDas Skript enthält eine Literaturliste.
Voraussetzungen / BesonderesDie Vorlesung wird auf Deutsch gehalten, das Skript und die Präsentationen sind auf Englisch.
227-2037-00LPhysical Modelling and Simulation Information W5 KP4GC. Hafner, J. Leuthold, J. Smajic
KurzbeschreibungThis module consists of (a) an introduction to fundamental equations of electromagnetics, mechanics and heat transfer, (b) a detailed overview of numerical methods for field simulations, and (c) practical examples solved in form of small projects.
LernzielBasic knowledge of the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. Knowledge of the main concepts of numerical methods for physical modelling and simulation. Ability (a) to develop own simple field simulation programs, (b) to select an appropriate field solver for a given problem, (c) to perform field simulations, (d) to evaluate the obtained results, and (e) to interactively improve the models until sufficiently accurate results are obtained.
InhaltThe module begins with an introduction to the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. After the introduction follows a detailed overview of the available numerical methods for solving electromagnetic, thermal and mechanical boundary value problems. This part of the course contains a general introduction into numerical methods, differential and integral forms, linear equation systems, Finite Difference Method (FDM), Boundary Element Method (BEM), Method of Moments (MoM), Multiple Multipole Program (MMP) and Finite Element Method (FEM). The theoretical part of the course finishes with a presentation of multiphysics simulations through several practical examples of HF-engineering such as coupled electromagnetic-mechanical and electromagnetic-thermal analysis of MEMS.
In the second part of the course the students will work in small groups on practical simulation problems. For solving practical problems the students can develop and use own simulation programs or chose an appropriate commercial field solver for their specific problem. This practical simulation work of the students is supervised by the lecturers.
227-0301-00LOptical Communication FundamentalsW6 KP2V + 1U + 1PJ. Leuthold
KurzbeschreibungThe path of an analog signal in the transmitter to the digital world in a communication link and back to the analog world at the receiver is discussed. The lecture covers the fundamentals of all important optical and optoelectronic components in a fiber communication system. This includes the transmitter, the fiber channel and the receiver with the electronic digital signal processing elements.
LernzielAn in-depth understanding on how information is transmitted from source to destination. Also the mathematical framework to describe the important elements will be passed on. Students attending the lecture will further get engaged in critical discussion on societal, economical and environmental aspects related to the on-going exponential growth in the field of communications.
Inhalt* Chapter 1: Introduction: Analog/Digital conversion, The communication channel, Shannon channel capacity, Capacity requirements.

* Chapter 2: The Transmitter: Components of a transmitter, Lasers, The spectrum of a signal, Optical modulators, Modulation formats.

* Chapter 3: The Optical Fiber Channel: Geometrical optics, The wave equations in a fiber, Fiber modes, Fiber propagation, Fiber losses, Nonlinear effects in a fiber.

* Chapter 4: The Receiver: Photodiodes, Receiver noise, Detector schemes (direct detection, coherent detection), Bit-error ratios and error estimations.

* Chapter 5: Digital Signal Processing Techniques: Digital signal processing in a coherent receiver, Error detection teqchniques, Error correction coding.

* Chapter 6: Pulse Shaping and Multiplexing Techniques: WDM/FDM, TDM, OFDM, Nyquist Multiplexing, OCDMA.

* Chapter 7: Optical Amplifiers : Semiconductor Optical Amplifiers, Erbium Doped Fiber Amplifiers, Raman Amplifiers.
SkriptLecture notes are handed out.
LiteraturGovind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010
Voraussetzungen / BesonderesFundamentals of Electromagnetic Fields & Bachelor Lectures on Physics.
401-5870-00LSeminar in Electromagnetics for CSE Information W4 KP2SC. Hafner, J. Leuthold
KurzbeschreibungVarious topics of electromagnetics, including electromagnetic theory, computational electromagnetics, electromagnetic wave propagation, applications from statics to optics. Traditional problems such as antennas, electromagnetic scattering, waveguides, resonators, etc. as well as modern topics such as photonic crystals, metamaterials, plasmonics, etc. are considered.
LernzielKnowledge of the fundamentals of electromagnetic theory, development and application of numerical methods for solving Maxwell equations, analysis and optimal design of electromagnetic structures
  • Erste Seite Vorherige Seite Seite  2  von  3 Nächste Seite Letzte Seite     Alle