Suchergebnis: Katalogdaten im Herbstsemester 2020

Mathematik Master Information
Wahlfächer
Für das Master-Diplom in Angewandter Mathematik ist die folgende Zusatzbedingung (nicht in myStudies ersichtlich) zu beachten: Mindestens 15 KP der erforderlichen 28 KP aus Kern- und Wahlfächern müssen aus Bereichen der angewandten Mathematik und weiteren anwendungsorientierten Gebieten stammen.
Wahlfächer aus Bereichen der reinen Mathematik
Auswahl: Algebra, Zahlentheorie, Topologie, diskrete Mathematik, Logik
NummerTitelTypECTSUmfangDozierende
401-3119-70Lp-Adic NumbersW4 KP2VP. Bengoechea Duro
KurzbeschreibungThis course is an introduction to the p-adic numbers. We will see how the field of p-adic numbers Q_p is build. We will explore the (strange) topology and the arithmetic of Q_p, as well as some elementary analytic concepts such as functions, continuity, integrals, etc. We will explain an algebraic and an analytic reasons of interest for the existence of p-adic numbers.
Lernziel
Inhalt- Absolute values on Q and Completions
- Topology and Arithmetic of Q_p, p-adic Integers
- Equations over p-adic numbers and Hensel's Lemma
- Local-global principle
- Hasse-Minkowski's Theorem on binary quadratic forms
- Elementary Analysis in Q_p
- the p-adic Riemann zeta function
Literatur"p-adic Numbers. An Introduction", Fernando Q. Gouvea (Springer)
"p-adic Numbers, p-adic Analysis, and Zeta-Functions", Neal Koblitz (Springer)
"p-adic numbers and Diophantine equations", Yuri Bilu (online notes 2013)
Voraussetzungen / BesonderesThe courses Topology, Measure and Integration, Algebra I/II are required prerequisites.
401-3059-00LKombinatorik II
Findet dieses Semester nicht statt.
W4 KP2GN. Hungerbühler
KurzbeschreibungDer Kurs Kombinatorik I und II ist eine Einführung in die abzählende Kombinatorik.
LernzielDie Studierenden sind in der Lage, kombinatorische Probleme einzuordnen und die adaequaten Techniken zu deren Loesung anzuwenden.
InhaltInhalt der Vorlesungen Kombinatorik I und II: Kongruenztransformationen der Ebene, Symmetriegruppen von geometrischen Figuren, Eulersche Funktion, Cayley-Graphen, formale Potenzreihen, Permutationsgruppen, Zyklen, Lemma von Burnside, Zyklenzeiger, Saetze von Polya, Anwendung auf die Graphentheorie und isomere Molekuele.
Auswahl: Geometrie
NummerTitelTypECTSUmfangDozierende
401-3533-70LDifferential Geometry IIIW4 KP2VU. Lang
KurzbeschreibungTopics in Riemannian geometry in the large: the structure of complete, non-compact Riemannian manifolds of non-negative sectional curvature, including Perelman's (1994) proof of the Cheeger-Gromoll soul conjecture; the Besson-Courtois-Gallot barycenter method (1996) and the proofs of the minimal entropy theorem and the Mostow rigidity theorem for rank one locally symmetric spaces.
Lernziel
401-4531-66LTopics in Rigidity Theory Information W6 KP3VM. Burger
KurzbeschreibungThe aim of this course is to give detailed proofs of Margulis' normal subgroup theorem and his superrigidity theorem for lattices in higher rank Lie groups.
LernzielUnderstand the basic techniques of rigidity theory.
InhaltThis course gives an introduction to rigidity theory, which is a set of techniques initially invented to understand the structure of a certain class of discrete subgroups of Lie groups, called lattices, and currently used in more general contexts of groups arising as isometries of non-positively curved geometries. A prominent example of a lattice in the Lie group SL(n, R) is the group SL(n, Z) of integer n x n matrices with determinant 1. Prominent questions concerning this group are:
- Describe all its proper quotients.
- Classify all its finite dimensional linear representations.
- More generally, can this group act by diffeomorphisms on "small" manifolds like the circle?
- Does its Cayley graph considered as a metric space at large scale contain enough information to recover the group structure?
In this course we will give detailed treatment for the answers to the first two questions; they are respectively Margulis' normal subgroup theorem and Margulis' superrigidity theorem. These results, valid for all lattices in simple Lie groups of rank at least 2 --like SL(n, R), with n at least 3-- lead to the arithmeticity theorem, which says that all lattices are obtained by an arithmetic construction.
Literatur- R. Zimmer: "Ergodic Theory and Semisimple groups", Birkhauser 1984.
- D. Witte-Morris: "Introduction to Arithmetic groups", available on Arxiv
- Y. Benoist: "Five lectures on lattices in semisimple Lie groups", available on his homepage.
- M.Burger: "Rigidity and Arithmeticity", European School of Group Theory, 1996, handwritten notes, will be put online.
Voraussetzungen / BesonderesFor this course some knowledge of elementary Lie theory would be good. We will however treat Lie groups by examples and avoid structure theory since this is not the point of the course nor of the techniques.
401-4141-70LCurves, Jacobians, and Modern Abel-Jacobi Theory Information W6 KP3VR. Pandharipande
Kurzbeschreibung
Lernziel
401-3057-00LEndliche Geometrien IIW4 KP2GN. Hungerbühler
KurzbeschreibungEndliche Geometrien I, II: Endliche Geometrien verbinden Aspekte der Geometrie mit solchen der diskreten Mathematik und der Algebra endlicher Körper. Inbesondere werden Modelle der Inzidenzaxiome konstruiert und Schliessungssätze der Geometrie untersucht. Anwendungen liegen im Bereich der Statistik, der Theorie der Blockpläne und der Konstruktion orthogonaler lateinischer Quadrate.
LernzielEndliche Geometrien I, II: Die Studierenden sind in der Lage, Modelle endlicher Geometrien zu konstruieren und zu analysieren. Sie kennen die Schliessungssätze der Inzidenzgeometrie und können mit Hilfe der Theorie statistische Tests entwerfen sowie orthogonale lateinische Quadrate konstruieren. Sie sind vertraut mit Elementen der Theorie der Blockpläne.
InhaltEndliche Geometrien I, II: Endliche Körper, Polynomringe, endliche affine Ebenen, Axiome der Inzidenzgeometrie, Eulersches Offiziersproblem, statistische Versuchsplanung, orthogonale lateinische Quadrate, Transformationen endlicher Ebenen, Schliessungsfiguren von Desargues und Pappus-Pascal, Hierarchie der Schliessungsfiguren, endliche Koordinatenebenen, Schiefkörper, endliche projektive Ebenen, Dualitätsprinzip, endliche Möbiusebenen, selbstkorrigierende Codes, Blockpläne
Literatur- Max Jeger, Endliche Geometrien, ETH Skript 1988

- Albrecht Beutelspacher: Einführung in die endliche Geometrie I,II. Bibliographisches Institut 1983

- Margaret Lynn Batten: Combinatorics of Finite Geometries. Cambridge University Press

- Dembowski: Finite Geometries.
Auswahl: Analysis
NummerTitelTypECTSUmfangDozierende
401-4355-70LElliptic Regularity TheoryW8 KP4VM. Struwe
KurzbeschreibungWe extend the theory developed in Functional Analysis II in various directions, including variants of the maximum principle, Harnack's inequality, L^p-theory, and systems. Certain limit cases will be discussed. Examples, including the harmonic map system, will illustrate the use of these methods.
Lernziel
LiteraturGiaquinta, Mariano: Introduction to regularity theory for nonlinear elliptic systems. Lectures in Mathematics ETH Zürich. Birkhäuser Verlag, Basel, 1993.

Gilbarg, David; Trudinger, Neil S.: Elliptic partial differential equations of second order. Springer-Verlag, Berlin, 2001.

Further references will be given in the lectures.
Auswahl: Weitere Gebiete
NummerTitelTypECTSUmfangDozierende
401-3502-70LReading Course Belegung eingeschränkt - Details anzeigen
To start an individual reading course, contact an authorised supervisor
Link
and register your reading course in myStudies.
W2 KP4ABetreuer/innen
KurzbeschreibungIn diesem Reading Course wird auf Eigeninitiative und auf individuelle Vereinbarung mit einem Dozenten/einer Dozentin hin ein Stoff durch eigenständiges Literaturstudium erarbeitet.
Lernziel
401-3503-70LReading Course Belegung eingeschränkt - Details anzeigen
To start an individual reading course, contact an authorised supervisor
Link
and register your reading course in myStudies.
W3 KP6ABetreuer/innen
KurzbeschreibungIn diesem Reading Course wird auf Eigeninitiative und auf individuelle Vereinbarung mit einem Dozenten/einer Dozentin hin ein Stoff durch eigenständiges Literaturstudium erarbeitet.
Lernziel
401-3504-70LReading Course Belegung eingeschränkt - Details anzeigen
To start an individual reading course, contact an authorised supervisor
Link
and register your reading course in myStudies.
W4 KP9ABetreuer/innen
KurzbeschreibungIn diesem Reading Course wird auf Eigeninitiative und auf individuelle Vereinbarung mit einem Dozenten/einer Dozentin hin ein Stoff durch eigenständiges Literaturstudium erarbeitet.
Lernziel
401-0000-00LCommunication in MathematicsW2 KP1VW. Merry
KurzbeschreibungDon't hide your Next Great Theorem behind bad writing.

This course teaches fundamental communication skills in mathematics: how to write clearly and how to structure mathematical content for different audiences, from theses, to preprints, to personal statements in applications. In addition, the course will help you establish a working knowledge of LaTeX.
LernzielKnowing how to present written mathematics in a structured and clear manner.
InhaltTopics covered include:

- Language conventions and common errors.
- How to write a thesis (more generally, a mathematics paper).
- How to use LaTeX.
- How to write a personal statement for Masters and PhD applications.
SkriptFull lecture notes will be made available on my website:

Link
Voraussetzungen / BesonderesThere are no formal mathematical prerequisites.
Wahlfächer aus Bereichen der angewandten Mathematik ...
vollständiger Titel:
Wahlfächer aus Bereichen der angewandten Mathematik und weiteren anwendungsorientierten Gebieten
Auswahl: Numerische Mathematik
NummerTitelTypECTSUmfangDozierende
401-4657-00LNumerical Analysis of Stochastic Ordinary Differential Equations Information Belegung eingeschränkt - Details anzeigen
Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods"
W6 KP3V + 1UD. Salimova
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
Applications to computational finance: Option valuation
SkriptThere will be English, typed lecture notes for registered participants in the course.
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 16, 2020.
401-4785-00LMathematical and Computational Methods in PhotonicsW8 KP4GH. Ammari
KurzbeschreibungThe aim of this course is to review new and fundamental mathematical tools, computational approaches, and inversion and optimal design methods used to address challenging problems in nanophotonics. The emphasis will be on analyzing plasmon resonant nanoparticles, super-focusing & super-resolution of electromagnetic waves, photonic crystals, electromagnetic cloaking, metamaterials, and metasurfaces
LernzielThe field of photonics encompasses the fundamental science of light propagation and interactions in complex structures, and its technological applications.

The recent advances in nanoscience present great challenges for the applied and computational mathematics community. In nanophotonics, the aim is to control, manipulate, reshape, guide, and focus electromagnetic waves at nanometer length scales, beyond the resolution limit. In particular, one wants to break the resolution limit by reducing the focal spot and confine light to length scales that are significantly smaller than half the wavelength.

Interactions between the field of photonics and mathematics has led to the emergence of a multitude of new and unique solutions in which today's conventional technologies are approaching their limits in terms of speed, capacity and accuracy. Light can be used for detection and measurement in a fast, sensitive and accurate manner, and thus photonics possesses a unique potential to revolutionize healthcare. Light-based technologies can be used effectively for the very early detection of diseases, with non-invasive imaging techniques or point-of-care applications. They are also instrumental in the analysis of processes at the molecular level, giving a greater understanding of the origin of diseases, and hence allowing prevention along with new treatments. Photonic technologies also play a major role in addressing the needs of our ageing society: from pace-makers to synthetic bones, and from endoscopes to the micro-cameras used in in-vivo processes. Furthermore, photonics are also used in advanced lighting technology, and in improving energy efficiency and quality. By using photonic media to control waves across a wide band of wavelengths, we have an unprecedented ability to fabricate new materials with specific microstructures.

The main objective in this course is to report on the use of sophisticated mathematics in diffractive optics, plasmonics, super-resolution, photonic crystals, and metamaterials for electromagnetic invisibility and cloaking. The book merges highly nontrivial multi-mathematics in order to make a breakthrough in the field of mathematical modelling, imaging, and optimal design of optical nanodevices and nanostructures capable of light enhancement, and of the focusing and guiding of light at a subwavelength scale. We demonstrate the power of layer potential techniques in solving challenging problems in photonics, when they are combined with asymptotic analysis and the elegant theory of Gohberg and Sigal on meromorphic operator-valued functions.

In this course we shall consider both analytical and computational matters in photonics. The issues we consider lead to the investigation of fundamental problems in various branches of mathematics. These include asymptotic analysis, spectral analysis, mathematical imaging, optimal design, stochastic modelling, and analysis of wave propagation phenomena. On the other hand, deriving mathematical foundations, and new and efficient computational frameworks and tools in photonics, requires a deep understanding of the different scales in the wave propagation problem, an accurate mathematical modelling of the nanodevices, and fine analysis of complex wave propagation phenomena. An emphasis is put on mathematically analyzing plasmon resonant nanoparticles, diffractive optics, photonic crystals, super-resolution, and metamaterials.
401-4427-70LRepresentation Theory in Signal AnalysisW4 KP2VF. Bartolucci
KurzbeschreibungThe scope of the course is to give an introduction to the theory of unitary representations of locally compact groups with a particular regard to the applications of this theory in signal analysis.
Lernziel
InhaltThe scope of the course is to give an introduction to the theory of
unitary representations of locally compact groups with a particular regard to
the applications of this theory in signal analysis. The course starts with an
overview of the measure theory on locally compact groups. Then, the fundamental
definitions and results in representation theory are presented (irreducible
unitary representations, Schur’s lemma, voice transforms, square-integrable
representations, reproducing formulae). We conclude the course showing that
some of the most important transforms in applied harmonic analysis such as the
Gabor transform, the wavelet transform and the shearlet transform are related
to square-integrable unitary representations.
Voraussetzungen / BesonderesPrerequisites: measure theory, topology, functional analysis, operator
theory, Fourier analysis
Auswahl: Wahrscheinlichkeitstheorie, Statistik
NummerTitelTypECTSUmfangDozierende
401-4607-70LA Medley of Advanced ProbabilityW4 KP2VW. Werner
KurzbeschreibungWe will review various topics of probability theory, with the goal to provide a short self-contained introduction to each of them, and try to describe the type of ideas and techniques that are used.
Exact topics will include (small bits of) Lévy processes, continuous-state branching processes, large deviation theory, large random matrices.
LernzielThe goal is for each of the topics that will be covered to provide:
- A general introduction to the subject
- An example of one of the main statements, and some of the ideas that go into the proof
- A detailed proof of one statement
Voraussetzungen / BesonderesPrerequisites: Martingales, Markov chains, Brownian motion, stochastic calculus.
401-3628-14LBayesian Statistics
Findet dieses Semester nicht statt.
W4 KP2V
KurzbeschreibungIntroduction to the Bayesian approach to statistics: decision theory, prior distributions, hierarchical Bayes models, empirical Bayes, Bayesian tests and model selection, empirical Bayes, Laplace approximation, Monte Carlo and Markov chain Monte Carlo methods.
LernzielStudents understand the conceptual ideas behind Bayesian statistics and are familiar with common techniques used in Bayesian data analysis.
InhaltTopics that we will discuss are:

Difference between the frequentist and Bayesian approach (decision theory, principles), priors (conjugate priors, noninformative priors, Jeffreys prior), tests and model selection (Bayes factors, hyper-g priors for regression),hierarchical models and empirical Bayes methods, computational methods (Laplace approximation, Monte Carlo and Markov chain Monte Carlo methods)
SkriptA script will be available in English.
LiteraturChristian Robert, The Bayesian Choice, 2nd edition, Springer 2007.

A. Gelman et al., Bayesian Data Analysis, 3rd edition, Chapman & Hall (2013).

Additional references will be given in the course.
Voraussetzungen / BesonderesFamiliarity with basic concepts of frequentist statistics and with basic concepts of probability theory (random variables, joint and conditional distributions, laws of large numbers and central limit theorem) will be assumed.
401-0625-01LApplied Analysis of Variance and Experimental DesignW5 KP2V + 1UL. Meier
KurzbeschreibungPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LernzielParticipants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R.
InhaltPrinciples of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.
LiteraturG. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000.
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held.
401-0649-00LApplied Statistical RegressionW5 KP2V + 1UM. Dettling
KurzbeschreibungThis course offers a practically oriented introduction into regression modeling methods. The basic concepts and some mathematical background are included, with the emphasis lying in learning "good practice" that can be applied in every student's own projects and daily work life. A special focus will be laid in the use of the statistical software package R for regression analysis.
LernzielThe students acquire advanced practical skills in linear regression analysis and are also familiar with its extensions to generalized linear modeling.
InhaltThe course starts with the basics of linear modeling, and then proceeds to parameter estimation, tests, confidence intervals, residual analysis, model choice, and prediction. More rarely touched but practically relevant topics that will be covered include variable transformations, multicollinearity problems and model interpretation, as well as general modeling strategies.

The last third of the course is dedicated to an introduction to generalized linear models: this includes the generalized additive model, logistic regression for binary response variables, binomial regression for grouped data and poisson regression for count data.
SkriptA script will be available.
LiteraturFaraway (2005): Linear Models with R
Faraway (2006): Extending the Linear Model with R
Draper & Smith (1998): Applied Regression Analysis
Fox (2008): Applied Regression Analysis and GLMs
Montgomery et al. (2006): Introduction to Linear Regression Analysis
Voraussetzungen / BesonderesThe exercises, but also the classes will be based on procedures from the freely available, open-source statistical software package R, for which an introduction will be held.

In the Mathematics Bachelor and Master programmes, the two course units 401-0649-00L "Applied Statistical Regression" and 401-3622-00L "Statistical Modelling" are mutually exclusive. Registration for the examination of one of these two course units is only allowed if you have not registered for the examination of the other course unit.
401-4521-70LGeometric Tomography - Uniqueness, Statistical Reconstruction and Algorithms Information Belegung eingeschränkt - Details anzeigen W4 KP2VJ. Hörrmann
KurzbeschreibungSelf-contained course on the theoretical aspects of the reconstruction of geometric objects from tomographic projection and section data.
LernzielIntroduction to geometric tomography and understanding of various theoretical aspects of reconstruction problems.
InhaltThe problem of reconstruction of an object from geometric information like X-ray data is a classical inverse problem on the overlap between applied mathematics, statistics, computer science and electrical engineering. We focus on various aspects of the problem in the case of prior shape information on the reconstruction object. We will answer questions on uniqueness of the reconstruction and also cover statistical and algorithmic aspects.
LiteraturR. Gardner: Geometric Tomography
F. Natterer: The Mathematics of Computerized Tomography
A. Rieder: Keine Probleme mit inversen Problemen
Voraussetzungen / BesonderesA sound mathematical background in geometry, analysis and probability is required though a repetition of relevant material will be included. The ability to understand and write mathematical proofs is mandatory.
401-4607-59LPercolation Theory Information W4 KP2VV. Tassion
KurzbeschreibungAn introduction to the percolation theory.
LernzielPercolation theory has many applications and is one of the most famous model to
describe phase transition phenomena in physics. One reason for this success is
the variety of mathematical tools, which allows for a precise and rigorous
description of the models. The objective of this course is to gain familiarity
with the methods of the percolation theory and to learn some of its important
results. The students will develop their background and intuition in
probability, and the course is particularly recommended to students with
additional interests in physics or graph theory.
InhaltDefinition of percolation. Standard tools: FKG, BK inequalities, Mixing
property, Russo's formula. Sharpness of the phase transition. Correlation
length and interpretations. Uniqueness of the infinite cluster. Critical
percolation in dimension 2. Supercritical percolation in dimension d>2,
Grimmett-Marstrand Theorem and consequences.
LiteraturB. Bollobas, O. Riordan: Percolation, CUP 2006
G. Grimmett: Percolation 2ed, Springer 1999
Voraussetzungen / BesonderesPreliminaries:
401-2604-00L Probability and Statistics (mandatory)
401-3601-00L Probability Theory (recommended)
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