Detailed information can be found in the course descriptions and in the module handbooks (in German only).

30.09.–02.10. and 04.10.; begins on 30.09. at 9h15 in HS Rundbau.

Teaching

Teacher: Nadine Große

Assistant: Jonah Reuß

Language: in German

02.10.–05.10.2024, begins at 9h in HS Rundbau.

Teaching

Teacher: Mirjam Hoferichter, Susanne Knies

Language: in German

Exercising the Basics

Teaching

Teacher: Fachschaft

Language: in German

Supervised Exercising

Teaching

Teacher: Fachschaft

Language: in German

Lecture: Di, Mi, 8-10h, HS Rundbau, Albertstr. 21

Tutorial: 2 hours, various dates

Teaching

Teacher: Michael Růžička

Assistant: Alexei Gazca

Language: in German

Content

Analysis I is one of the two basic lectures in the mathematics course. It deals with concepts based on the notion of limit. The central topics are: induction, real and complex numbers, convergence of sequences and series, completeness, exponential function and trigonometric functions, continuity, derivation of functions of one variable and regulated integrals.

Previous
knowledge

Required: High school mathematics. \ Attendance of the preliminary course (for students in mathematics) is recommended.

Usability

Analysis (2HfB21, BSc21, MEH21, MEB21)

Analysis I (BScInfo19, BScPhys20)

Linear Algebra I

Lecture: Mo, Do, 8-10h, HS Rundbau, Albertstr. 21

Tutorial: 2 hours, various dates

Teaching

Teacher: Stefan Kebekus

Assistant: Marius Amann

Language: in German

Content

Linear Algebra I is one of the two introductory lectures in the mathematics degree program that form the basis for further courses. Topics covered include: fundamental concepts (in particular fundamental concepts of set theory and equivalence relations), groups, fields, vector spaces over arbitrary fields, basis and dimension, linear mappings and transformation matrix, matrix calculus, linear systems of equations, Gaussian elimination, linear forms, dual space, quotient vector spaces and homomorphism theorem, determinant, eigenvalues, polynomials, characteristic polynomial, diagonalizability, affine spaces. The background to the mathematical content is explained in terms of ideas and the history of mathematics.

Previous
knowledge

Required: High school mathematics. \ Attendance of the preliminary course (for students in mathematics) is recommended.

Usability

Linear Algebra (2HfB21, BSc21, MEH21)

Linear Algebra (MEB21)

Linear Algebra I (BScInfo19, BScPhys20)

Lecture: Mi, 14-16h, HS Weismann-Haus, Albertstr. 21a

Tutorial: 2 hours, every other week, various dates

Teaching

Teacher: Sören Bartels

Assistant: Tatjana Schreiber

Language: in German

Content

Numerics is a sub-discipline of mathematics that deals with the practical solution of mathematical problems. As a rule, problems are not solved exactly but approximately, for which a sensible compromise between accuracy and computational effort must be found. The first part of the two-semester course focuses on questions of linear algebra such as solving linear systems of equations and determining the eigenvalues of a matrix. Attendance at the accompanying practical exercises ({\em Praktische Übung zur Numerik}) is recommended. These take place every 14 days, alternating with the lecture's tutorial.

Previous
knowledge

Required: Linear Algebra~I \ Recommended: Linear Algebra~II and Analysis~I (required for Numerics~II)

Usability

Numerics (BSc21)

Numerics (2HfB21, MEH21)

Numerics I (MEB21)

Lecture: Fr, 10-12h, HS Weismann-Haus, Albertstr. 21a

Tutorial: 2 hours, every other week, various dates

Teaching

Teacher: Angelika Rohde

Assistant: Johannes Brutsche

Language: in German

Content

Stochastic is, to put it loosely, the “mathematics of chance”, about which---possibly contrary to first impressions---many precise and not at all random statements can be formulated and proven. The aim of the lecture is to give an introduction to stochastic modeling, to explain some basic concepts and results of Stochastic and to illustrate them with examples. It is also intended as a motivating preparation for the lecture “Probability Theory” in the summer semester, especially for students in the B.Sc. in Mathematics. Topics covered include: Discrete and continuous random variables, probability spaces and measures, combinatorics, expected value, variance, correlation, generating functions, conditional probability, independence, weak law of large numbers, central limit theorem. The lecture Elementary Probability Theory~II in the summer semester will mainly be devoted to statistical topics. If you are interested in a practical, computer-supported implementation of individual lecture contents, participation in the regularly offered practical excercise “Praktischen Übung Stochastik" is also recommended (in parallel or subsequently).

Previous
knowledge

Required: Linear Algebra~I, Analysis~I and II. \ Note that Linear Algebra~I can be attended in parallel.

Usability

Elementary Probabilty Theory (2HfB21, MEH21)

Elementary Probability Theory I (BSc21, MEB21, MEdual24)

Further Chapters in Analysis

Lecture: Mi, 8-10h, HS Weismann-Haus, Albertstr. 21a

Tutorial: 2 hours, various dates

Teaching

Teacher: Nadine Große

Assistant: Jonah Reuß

Language: in German

Content

\textit{Multiple integration:} Jordan content in \(\mathbb R^n\), Fubini's theorem, transformation theorem, divergence and rotation of vector fields, path and surface integrals in \(\mathbb R^3\), Gauss' theorem, Stokes' theorem.\ \textit{Complex analysis:} Introduction to the theory of holomorphic functions, Cauchy's integral theorem, Cauchy's integral formula and applications.

Previous
knowledge

Required: Analysis~I and II, Linear Algebra~I and II

Usability

Further Chapters in Analysis (MEd18, MEH21, MEdual24)

Lecture: Di, Do, 8-10h, HS II, Albertstr. 23b

Tutorial: 2 hours, date to be determined

Computer exercise: 2 hours, date to be determined

Teaching

Teacher: Moritz Diehl, Patrick Dondl, Angelika Rohde

Assistant: Ben Deitmar, Coffi Aristide Hounkpe

Language: in English

Content

This course provides an introduction into the basic concepts, notions, definitions and results in probability theory, numerics and optimization, accompanied with programming projects in Python. Besides deepen mathematical skills in principle, the course lays the foundation of further classes in these three areas.

Previous
knowledge

None that go beyond admission to the degree programme.

Usability

Basics in Applied Mathematics (MScData24)

Lecture: Di, Do, 10-12h, HS Weismann-Haus, Albertstr. 21a

Tutorial: 2 hours, various dates

Teaching

Teacher: Wolfgang Soergel

Assistant: Damian Sercombe

Language: in German

Content

This lecture continues the linear algebra courses. It treats groups, rings, fields and applications in the number theory and geometry. The highlights of the lecture are the classification of finite fields, the impossibility of the trisection of angles with circle and ruler, the non-existence of a solution formula for the general equations of fifth degree and the quadratic reciprocity law.

Previous
knowledge

Required: Linear Algebra~I and II

Usability

Algebra and Number Theory (2HfB21, MEH21)

Compulsory Elective in Mathematics (BSc21)

Introduction to Algebra and Number Theory (MEB21)

Algebra and Number Theory (MEdual24)

Pure Mathematics (MSc14)

Elective (MSc14)

Elective (MScData24)

Algebraic Number Theory

Lecture: Di, Do, 12-14h, HS II, Albertstr. 23b

Tutorial: 2 hours, date to be determined

Teaching

Teacher: Abhishek Oswal

Assistant: Andreas Demleitner

Language: in English

Content

Short description of topics: Number fields, Prime decomposition in Dedekind domains, Ideal class groups, Unit groups, Dirichlet's unit theorem, local fields, valuations, decomposition and inertia groups, introduction to class field theory.

Previous
knowledge

Required: Algebra and Number Theory

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Mathematical Concentration (MEd18, MEH21)

Pure Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective (MScData24)

Lecture: Mo, 12-14h, HS Rundbau, Albertstr. 21, Mi, 10-12h, HS Weismann-Haus, Albertstr. 21a

Tutorial: 2 hours, various dates

Teaching

Teacher: Patrick Dondl

Assistant: Oliver Suchan

Language: in German

Content

Lebesgue measure and measure theory, Lebesgue integral on measure spaces and Fubini's theorem, Fourier series and Fourier transform, Hilbert spaces. Differential forms, their integration and outer derivative. Stokes' theorem and Gauss' theorem.

Previous
knowledge

Required: Analysis I and II, Linear Algebra I

Usability

Elective (Option Area) (2HfB21)

Analysis III (BSc21)

Mathematical Concentration (MEd18, MEH21)

Elective in Data (MScData24)

Lecture: Mo, Mi, 14-16h, HS II, Albertstr. 23b

Tutorial: 2 hours, date to be determined

Teaching

Teacher: Sebastian Goette

Assistant: Mikhail Tëmkin

Language: in German

Content

Differential geometry, especially Riemannian geometry, deals with the geometric properties of curved spaces. Such spaces also occur in other areas of mathematics and physics, for example in geometric analysis, theoretical mechanics and the general theory of relativity.

Previous
knowledge

Required: Analysis~I–III, Lineare Algebra~I and II \ Recommended: Analysis of Curves and Surfaces ("Kurven und Flächen"), Topology

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Mathematical Concentration (MEd18, MEH21)

Pure Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective (MScData24)

Lecture: Mo, Mi, 12-14h, HS II, Albertstr. 23b

Tutorial: 2 hours, date to be determined

Teaching

Teacher: Guofang Wang

Assistant: Christine Schmidt

Language: in German

Content

A large number of different problems from the natural sciences and geometry lead to partial differential equations. Consequently, there can be no talk of an all-encompassing theory. Nevertheless, there is a clear picture for linear equations, which is based on three prototypes: the potential equation \(-\Delta u = f\), the heat equation \(u_t - \Delta u = f\) and the wave equation \(u_{tt} - \Delta u = f\), which we will examine in the lecture.

Previous
knowledge

Required: Analysis III \ Recommended: Complex Analysis ({\em Funktionentheorie})

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Mathematical Concentration (MEd18, MEH21)

Pure Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective (MScData24)

Lecture: Di, Do, 10-12h, SR 226, Hermann-Herder-Str. 10

Tutorial: 2 hours, date to be determined

Teaching

Teacher: Sören Bartels

Assistant: Vera Jackisch

Language: in English

Content

The aim of this course is to give an introduction into theory of linear partial differential equations and their finite difference as well as finite element approximations. Finite element methods for approximating partial differential equations have reached a high degree of maturity, and are an indispensable tool in science and technology. We provide an introduction to the construction, analysis, and implementation of finite element methods for different model problems. We will address elementary properties of linear partial differential equations along with their basic numerical approximation, the functional-analytical framework for rigorously establishing existence of solutions, and the construction and analysis of basic finite element methods.

Previous
knowledge

Required: Analysis~I and II, Linear Algebra~I and II as well as knowledge about higher-dimensional integration (e.g. from Analysis~III or Extensions of Analysis) \ Recommended: Numerics for differential equations, Functional analysis

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Mathematical Concentration (MEd18, MEH21)

Applied Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Advanced Lecture in Numerics (MScData24)

Elective in Data (MScData24)

Lecture: Di, Mi, 16-18h, HS II, Albertstr. 23b

Tutorial: 2 hours, date to be determined

Teaching

Teacher: David Criens

Assistant: Eric Trébuchon

Language: in German

Content

Complex analysis deals with functions \(f : \mathbb C \to \mathbb C\) , which map complex numbers to complex numbers. Many concepts of Analysis~I can be directly transferred to this case, e.\,g. the definition of differentiability. One might expect that this would lead to a theory analogous to Analysis~I but much more is true: in many respects you get a more elegant and simpler theory. For example, complex differentiability on an open set implies that a function is even infinitely often differentiable, and this is further consistent with analyticity. For real functions, all these notions are different. However, some new ideas are also necessary: For real numbers \(a\), \(b\) one integrates for \[\int_a^b f(x) \mathrm dx\] over the elements of the interval \([a, b]\) or \([b, a]\). However, if \(a\), \(b\) are complex numbers, it is no longer so clear clear how such an integral is to be calculated. One could, for example, in the complex numbers along the line that connects \(a, b \in \mathbb C\), or along another curve that leads from \(a\) to \(b\). Does this lead to a well-defined integral term or does such a curve integral depend on the choice of the curve?

Previous
knowledge

Required: Analysis I+II, Linear Algebra I

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Mathematical Concentration (MEd18, MEH21)

Pure Mathematics (MSc14)

Elective (MSc14)

Elective (MScData24)

Lecture: Mo, Mi, 14-16h, SR 404, Ernst-Zermelo-Str. 1

Teaching

Teacher: Ernst August v. Hammerstein

Assistant: Sebastian Stroppel

Language: in English

Content

The lecture builds on basic knowledge about Probability Theory. The fundamental problem of statistics is to infer from a sample of observations as precise as possible statements about the data-generating process or the underlying distributions of the data. For this purpose, the most important methods from statistical decision theory such as test and estimation methods are introduced in the lecture. \\ Key words hereto include Bayes estimators and tests, Neyman-Pearson test theory, maximum likelihood estimators, UMVU estimators, exponential families, linear models. Other topics include ordering principles for reducing the complexity of models (sufficiency and invariance). Statistical methods and procedures are used not only in the natural sciences and medicine, but in almost all areas in which data is collected and analyzed This includes, for example, economics (“econometrics”) and the social sciences (especially psychology). However, in the context of this lecture, we will focus less on applications, but---as the name suggests---more on the mathematical justification of the methods.

Previous
knowledge

Required: Probability Theory (in particular measure theory and conditional probabilities/expectations)

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Applied Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Advanced Lecture in Stochastics (MScData24)

Elective in Data (MScData24)

Set Theory – Independence Proofs

Lecture: Di, Do, 12-14h, SR 404, Ernst-Zermelo-Str. 1

Tutorial: 2 hours, date to be determined

Teaching

Teacher: Maxwell Levine

Assistant: Hannes Jakob

Language: in English

Content

How does one prove that something cannot be proved? More precisely, how does one prove that a particular statement does not follow from a particular collection of axioms?

These questions are often asked with respect to the axioms most commonly used by mathematicians: the axioms of Zermelo-Fraenkel set theory, or ZFC for short. In this course, we will develop the conceptual tools needed to understand independence proofs with respect to ZFC. On the way we will develop the theory of ordinal and cardinal numbers, the basics of inner model theory, and the method of forcing. In particular, we will show that Cantor's continuum hypothesis, the statement that \(2^{\aleph_0}=\aleph_1\), is independent of ZFC.

Previous
knowledge

Required: Mathematical Logic

Compulsory Elective in Mathematics (BSc21)

Mathematical Concentration (MEd18, MEH21)

Pure Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective (MScData24)

Lecture: Di, Do, 10-12h, HS II, Albertstr. 23b

Tutorial: 2 hours, date to be determined

Teaching

Teacher: Annette Huber-Klawitter, Amador Martín Pizarro

Assistant: Christoph Brackenhofer

Language: in German

Content

Semi-algebraic geometry is about properties of subsets of \(**R**^n\), which are given by inequalities of the form
[ f(x*1, \dots, x*n)\geq 0]
for polynomials \(f\in**R**[X_1,\dots,X_n]\).

The theory has many different facets. On the one hand, it can be seen as a version of algebraic geometry over \(\mathbf{R}\) (or even more generally over so-called real closed fields). On the other hand, the properties of these fields are a central tool for the model-theoretic proof of Tarski-Seidenberg's theorem on quantifier elimination in real closed fields. Geometrically, this is interpreted as a projection theorem.

From this theorem, a proof of Hilbert's 17th problem easily follows, which was solved by Artin in 1926.

\textit{Is every real polynomial \(P \in \mathbf{R}[x_1, \dots, x_n]\), which takes a non-negative value for every \(n\)-tuple in \(\mathbf{R}^n\), a sum of squares of rational functions (i.e., quotients of polynomials)?}

In the lecture, we will explore both aspects. Necessary tools from commutative algebra or model theory will be discussed according to the prior knowledge of the audience.

Previous
knowledge

Required: Algebra and Number Theory \ Recommended: Knowledge in commutative algebra and algebraic geometry (cf. Kommutative Algebra und Einführung in die algebraische Geometrie), model theory

Compulsory Elective in Mathematics (BSc21)

Mathematical Concentration (MEd18, MEH21)

Pure Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective (MScData24)

Theory and Numerics for Partial Differential Equations – Nonlinear Problems

Lecture (four hours)

Tutorial: 2 hours, date to be determined

Tutorial: 2 hours, date to be determined

Teaching

Teacher: Sören Bartels, Patrick Dondl

Language: in English

Content

The lecture addresses the development and analysis of numerical methods for the approximation of certain nonlinear partial differential equations. The considered model problems include harmonic maps into spheres, total-variation regularized minimization problems, and nonlinear bending models. For each of the problems, a suitable finite element discretization is devised, its convergence is analyzed and iterative solution procedures are developed. The lecture is complemented by theoretical and practical lab tutorials in which the results are deepened and experimentally tested.

Previous
knowledge

Required: Introduction to Theory and Numerics for PDEs or Introduction to PDEs

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Applied Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Advanced Lecture in Numerics (MScData24)

Elective in Data (MScData24)

Questions sesssion / flipped classroom: Mo, 10-12h, HS II, Albertstr. 23b

Letcure (4 hours): asynchronous videos

Teaching

Teacher: Peter Pfaffelhuber

Assistant: Samuel Adeosun

Language: in English

Content

A stochastic process \((X_t)_{t\in I}\) is nothing more than a family of random variables, where \(I\) is some index set modeling time. Simple examples are random walks, Markov chains, Brownian motion and derived processes. The latter play a particularly important role in the modeling of financial mathematics or questions from the sciences. We will first deal with martingales, which describe fair games. After constructing the Poisson process and Brownian motion, we will focus on properties of Brownian motion. Infinitesimal characteristics of a Markov process are described by generators, which allows a connection to the theory of partial differential equations. Finally, a generalization of the law of large numbers is discussed with the ergodic theorem for stationary stochastic processes. Furthermore, insights are given into a few areas of application, such as biomathematics or random graphs.

Previous
knowledge

Required: Probability Theory I

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Applied Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Advanced Lecture in Stochastics (MScData24)

Elective in Data (MScData24)

Lecture: Mo, Mi, 12-14h, SR 404, Ernst-Zermelo-Str. 1

Tutorial: 2 hours, date to be determined

Teaching

Teacher: Thorsten Schmidt

Assistant: Moritz Ritter

Language: in English

Content

This lecture marks the culmination of our series on probability theory, achieving the ultimate goal of this series: the combination of stochastic analysis and financial mathematics---a field that has yielded an amazing wealth of fascinating results since the 1990s. The core is certainly the application of semimartingale theory to financial markets culminating in the fundamental theorem of asset pricing. This results is used everywhere in financial markets for arbitrage-free pricing.

After this we look into modern forms of stochastic analysis covering neural SDEs, signature methods, uncertainty and term structure models. The lecture will conclude with an examination of the latest applications of machine learning in financial markets and the reciprocal influence of stochastic analysis on machine learning.

Previous
knowledge

Required: Probability Theory II (Stochastic Processes)

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Applied Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Advanced Lecture in Stochastics (MScData24)

Elective in Data (MScData24)

Reading courses

Teaching

Teacher: Alle Professor:innen und Privatdozent:innen des Mathematischen Instituts

Language: Talk/participation possible in German and English

Content

In a reading course, the material of a four-hour lecture is studied in supervised self-study. In rare cases, this may take place as part of a course; however, reading courses are not usually listed in the course catalog. If you are interested, please contact a professor or a private lecturer before the start of the course; typically, this will be the supervisor of your Master's thesis, as the reading course ideally serves as preparation for the Master's thesis (both in the M.Sc. and the M.Ed. programs).

The content of the reading course, the specific details, and the coursework requirements will be determined by the supervisor at the beginning of the lecture period. The workload should be equivalent to that of a four-hour lecture with exercises.

Usability

Reading Course (MEd18, MEH21)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Lecture: Mo, 14-16h, SR 127, Ernst-Zermelo-Str. 1

Tutorial: 2 hours, date to be determined

Teaching

Teacher: Xuwen Zhang

Language: in English

Content

We will study functions of bounded variation, which are functions whose weak first partial derivatives are Radon measures. This is essentially the weakest definition of a function to be differentiable in the measure-theoretic sense. After discussing the basic properties of them, we move on to the study of sets of finite perimeter, which are Lebesgue measurable sets in the Euclidean space whose indicator functions are BV functions. Sets of finite perimeter are fundamental in the modern Calculus of Variations as they generalize in a natural measure-theoretic way the notion of sets with regular boundaries and possess nice compactness, thus appearing in many Geometric Variational problems. If time permits, we will discuss the (capillary) sessile drop problem as one important application.

Previous
knowledge

Required: Basic knowledge in measure theory and analysis is required.

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Pure Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective (MScData24)

Lecture: Mo, 10-12h, HS 3042, KG III

Teaching

Teacher: Eva Lütkebohmert-Holtz

Assistant: Hongyi Shen

Language: in English

Content

This course covers an introduction to financial markets and products. Besides futures and standard put and call options of European and American type we also discuss interest-rate sensitive instruments such as swaps.

For the valuation of financial derivatives we first introduce financial models in discrete time as the Cox--Ross--Rubinstein model and explain basic principles of risk-neutral valuation. Finally, we will discuss the famous Black--Scholes model which represents a continuous time model for option pricing.

Previous
knowledge

Required: Elementary Probability Theory~I

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Supplementary Module in Mathematics (MEd18)

Applied Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective in Data (MScData24)

Lecture: Do, 14-16h, SR 404, Ernst-Zermelo-Str. 1

Exercise session: Di, 8-10h, SR 127, Ernst-Zermelo-Str. 1

Teaching

Teacher: Maximilian Stegemeyer

Language: in English

Content

Lie groups and operations of Lie groups play a central role in geometry and topology. They can be used to describe continuous symmetries, one of the most important concepts of mathematics and physics. Exploiting symmetries, e.g. when describing homogeneous spaces, makes it easier to solve many specific problems and often provides a deeper insight into the structures examined. In addition, the geometry and topology of Lie groups and homogeneous spaces is of great interest.

In this lecture, we start with introducing the basic theory of Lie groups and Lie algebras, especially with insights into the structure theory of Lie algebras. In the second part we will look at homogeneous spaces with a special focus on Riemannian symmetric spaces. The latter form an important class of examples of Riemannian manifolds. In addition to the Lie-theoretical aspects, a special focus will always be on the homogeneous Riemannian metrics of the respective spaces.

Previous
knowledge

Required: Differential geometry~I

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Pure Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective (MScData24)

Lecture: Do, 12-14h, SR 226, Hermann-Herder-Str. 10

Tutorial: 2 hours, date to be determined

Teaching

Teacher: David Criens

Assistant: Dario Kieffer

Language: in English

Content

The class of Markov chains is an important class of (discrete-time) stochastic processes that are used frequently to model for example the spread of infections, queuing systems or switches of economic scenarios. Their main characteristic is the Markov property, which roughly means that the future depends on the past only through the current state. In this lecture we provide the mathematical foundation of the theory of Markov chains. In particular, we learn about path properties, such as recurrence and transience, state classifications and discuss convergence to the equilibrium. We also study extensions to continuous time. On the way we discuss applications to biology, queuing systems and resource management. If the time allows, we also take a look at Markov chains with random transition probabilities, so-called random walks in random environment, which is a prominent model in the field of random media.

Previous
knowledge

Required: Elementary Probability Theory~I \ Recommended: Analysis~III, Probability Theory~I

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Supplementary Module in Mathematics (MEd18)

Applied Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective in Data (MScData24)

Exercise session: Mi, 10-12h, HS II, Albertstr. 23b

Teaching

Teacher: Peter Pfaffelhuber

Assistant: Samuel Adeosun

Language: in English

Content

Measure Theory is the foundation of advanced probability theory. In this course, we build on knowledge in analysis and provide all necessary results for later classes in statistics, probabilistic machine learning and stochastic processes. It contains set systems, constructions of measures using outer measures, the integral, and product measures.

Previous
knowledge

Required: Basic courses in analysis, and an understanding of mathematical proofs.

Usability

Elective in Data (MScData24)

Mathematical Physics

Lecture: Di, 16-18h, SR 403, Ernst-Zermelo-Str. 1

Teaching

Teacher: Wolfgang Soergel

Language: in German

Content

Introduction to classic mechanics from the point of view of mathematics. We start with the mathematical modelling of space and time. Then we discuss Newton's equations of movement, physical systems with compulsory conditions, the D'Alembert principle, the Hamilton formalism and its derivation from the Newton's equations and applications of Hamilton formalism.

Previous
knowledge

Required: Analysis III

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Elective (BSc21)

Supplementary Module in Mathematics (MEd18)

Lecture: Di, Fr, 12-14h, SR 226, Hermann-Herder-Str. 10

Tutorial: 2 hours, date to be determined

Computer exercise: 2 hours, date to be determined

This course takes only place in the first half of the semester, until end of November.

Teaching

Teacher: Diyora Salimova

Assistant: Ilkhom Mukhammadiev

Language: in English

Content

The aim of this course is to enable the students to carry out simulations and their mathematical analysis for stochastic models originating from applications such as mathematical finance and physics. For this, the course teaches a decent knowledge on stochastic differential equations (SDEs) and their solutions. Furthermore, different numerical methods for SDEs, their underlying ideas, convergence properties, and implementation issues are studied.

Previous
knowledge

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

Usability

Elective (Option Area) (2HfB21)

Compulsory Elective in Mathematics (BSc21)

Supplementary Module in Mathematics (MEd18)

Applied Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective in Data (MScData24)

Tutorial / flipped classroom: Di, 14-16h, HS II, Albertstr. 23b

Teaching

Teacher: Moritz Diehl

Assistant: Florian Messerer

Language: in English

Content

The aim of the course is to give an introduction to numerical methods for the solution of optimal control problems in science and engineering. The focus is on both discrete time and continuous time optimal control in continuous state spaces. It is intended for a mixed audience of students from mathematics, engineering and computer science.

The course covers the following topics:

- Introduction to Dynamic Systems and Optimization
- Rehearsal of Newton-type methods and Numerical Optimization
- Algorithmic Differentiation
- Discrete Time Optimal Control
- Dynamic Programming
- Continuous Time Optimal Control
- Numerical Simulation Methods
- Hamilton–Jacobi–Bellmann Equation
- Pontryagin and the Indirect Approach
- Direct Optimal Control
- Real-Time Optimization for Model Predictive Control

The lecture is accompanied by intensive weekly computer exercises offered both in MATLAB and Python (6~ECTS) and an optional project (3~ECTS). The project consists in the formulation and implementation of a self-chosen optimal control problem and numerical solution method, resulting in documented computer code, a project report, and a public presentation.

Previous
knowledge

Required: Analysis~I and II, Linear Algebra~I and II \ Recommended: Numerics I, Ordinary Differential Equations, Numerical Optimization

Compulsory Elective in Mathematics (BSc21)

Supplementary Module in Mathematics (MEd18)

Applied Mathematics (MSc14)

Mathematics (MSc14)

Concentration Module (MSc14)

Elective (MSc14)

Elective in Data (MScData24)

Introduction to Mathematics Education

Mo 10-12h, SR 226, Hermann-Herder-Str. 10, Fr, 8-10h, SR 127, Ernst-Zermelo-Str. 1

Fr, 14-16h, SR 127, Ernst-Zermelo-Str. 1

Teaching

Teacher: Katharina Böcherer-Linder

Language: in German

Content

Mathematics didactic principles and their learning theory foundations and possibilities of teaching implementation (also e.g. with the help of digital media). \\ Theoretical concepts on central mathematical thinking activities such as concept formation, modeling, problem solving and reasoning. \\ Mathematics didactic constructs: Barriers to understanding, pre-concepts, basic ideas, specific difficulties with selected mathematical content. \\ Concepts for dealing with heterogeneity, taking into account subject-specific characteristics particularities (e.g. dyscalculia or mathematical giftedness).\\ Levels of conceptual rigour and formalization as well as their age-appropriate implementation.

Previous
knowledge

Required: Analysis~I, Linear Algebra~I

Usability

(Introduction to) Mathematics Education (2HfB21, MEH21, MEB21, MEdual24)

Mathematics Education ‒ Functions and Analysis

Seminar: Do, 9-12h, SR 404, Ernst-Zermelo-Str. 1

Teaching

Teacher: Katharina Böcherer-Linder

Language: in German

Content

Exemplary implementations of the theoretical concepts of central mathematical thought processes such as concept formation, modeling, problem solving and reasoning for the content areas of functions and analysis. \\ Barriers to understanding, pre-concepts, basic ideas, specific difficulties for the content areas of functions and analysis. \\ Fundamental possibilities and limitations of media, in particular of computer-aided mathematical tools mathematical tools and their application for the content areas of functions and analysis. Analysis of individual mathematical learning processes and errors as well as development individual support measures for the content areas of functions and analysis.

Previous
knowledge

Required: Introduction to the didactics of mathematics, Knowledge about analysis and numerics

Usability

Mathematics Education for Specific Areas of Mathematics (MEd18, MEH21, MEB21)

Mathematics Education ‒ Probability Theory and Algebra

Seminar: Fr, 9-12h, SR 226, Hermann-Herder-Str. 10

Teaching

Teacher: Anika Dreher

Language: in German

Content

Exemplary implementations of the theoretical concepts of central mathematical thought processes such as concept formation, modeling, problem solving and reasoning for the content areas of stochastics and algebra. \\ Barriers to understanding, pre-concepts, basic ideas, specific difficulties for the content areas of stochastics and algebra.\ Basic possibilities and limitations of media, especially computer-based mathematical tools and their mathematical tools and their application for the content areas of stochastics and algebra. and algebra. \\ Analysis of individual mathematical learning processes and errors as well as development individual support measures for the content areas of stochastics and algebra.

Previous
knowledge

Required: Introduction to the didactics of mathematics, knowledge from stochastics and algebra.

Usability

Mathematics Education for Specific Areas of Mathematics (MEd18, MEH21, MEB21)

Mathematics education seminar: Media Use in Teaching Mathematics

Seminar: Mi, 15-18h, SR 127, Ernst-Zermelo-Str. 1

Teaching

Teacher: Jürgen Kury

Language: in German

Content

The use of teaching media in mathematics lessons wins both at the level of lesson planning and lesson realization in importance. Against the background of constructivist learning theories shows that the reflective use of computer programs, among other things mathematical concept formation in the long term. For example experimenting with computer programs allows mathematical structures to be discovered, without this being overshadowed by individual routine operations (such as term transformation) would be covered up. This has far-reaching consequences for mathematics lessons. For this reason, this seminar aims to provide students the necessary decision-making and action skills to prepare future mathematics teachers for their professional activities. Starting from initial considerations about lesson planning, computers and tablets with regard to their respective didactic potential and tested with learners during a classroom visit. The exemplary systems presented are:

- dynamic geometry Software: Geogebra
- Spreadsheets: Excel
- Apps for Smartphones and tablets

The students should develop teaching sequences, which will then be tested and reflected on with pupils (where this will be possible).

Previous
knowledge

Recommended: Basic courses in mathematics

Usability

Supplementary Module in Mathematics Education (MEd18, MEH21, MEB21)

Mathematics education seminars at Freiburg University of Education

Teaching

Teacher: Teachers of the PH Freiburg

Language: in German

Usability

Supplementary Module in Mathematics Education (MEd18, MEH21, MEB21)

Module "Research in Mathematics Education"

Mo 14-16h, 16-19h, Raum noch nicht bekannt, PH Freiburg

Registration: see course descriptions

Teaching

Teacher: Teachers of the PH Freiburg, Frank Reinhold

Language: in German

Content

The three related courses of the module prepare students for an empirical Master thesis in mathematics didactics. The course is jointly designed by all professors at the PH with mathematics didactics research projects at secondary levels 1 and 2 and is carried out by one of these researchers. Afterwards, students have the opportunity to start Master thesis with one of these supervisors - usually integrated into larger ongoing research projects.

The first course of the module provides an introduction to strategies of empirical didactic research (research questions, research status, research designs). Students deepen their skills in scientific research and the evaluation of subject-specific didactic research. In the second course (in the last third of the semester) students are introduced to central qualitative and quantitative research methods through concrete work with existing data (interviews, student products, experimental data), students are introduced to central qualitative and quantitative research methods. The third course is an accompanying seminar for the Master thesis.

The main objectives of the module are the ability to receive mathematics didactic research in order to didactic research to clarify questions of practical relevance and to plan an empirical mathematics didactics Master thesis. It will be held as a mixture of seminar, development of research topics in groups and active work with research data. Recommended literature will be depending on the research topics offered within the respective courses. The parts can also be attended in different semesters, for example part~1 in the second Master semester and part~2 in the compact phase of the third Master semester after the practical semester.

Usability

Research in Mathematics Education (MEd18, MEH21, MEB21)

Teaching

Organisation: Susanne Knies

Language: in German

Content

What characterizes a good tutorial? This question will be discussed in the first workshop and tips and suggestions will be given. Experiences will be shared in the second workshop.

Usability

Elective (Option Area) (2HfB21)

Elective (BSc21)

Elective (MSc14)

Elective (MScData24)

Computer exercises for Introduction to Theory and Numerics of Partial Differential Equations

Computer exercise: 2 hours, date to be determined

Teaching

Teacher: Sören Bartels

Assistant: Vera Jackisch

Language: in English

Content

The computer tutorial accompanies the lecture with programming exercises.

Previous
knowledge

See the lecture – additionally: programming knowledge.

Usability

Elective (Option Area) (2HfB21)

Elective (BSc21)

Supplementary Module in Mathematics (MEd18)

Elective (MSc14)

Elective (MScData24)

Computer exercises in Numerics

Teaching

Teacher: Sören Bartels

Assistant: Tatjana Schreiber

Language: in German

Content

In the computer tutorial accompanying the Numerics (first term) lecture the algorithms developed and analyzed in the lecture are put into practice and and tested experimentally. The implementation is carried out in the programming languages Matlab, C++ and Python. Elementary programming knowledge is assumed.

Previous
knowledge

See the lecture {\em Numerics I} (which should be attended in parallel or should already have been completed). \ Additionally: Elementary programming knowledge.

Usability

Computer Exercise (2HfB21, MEH21, MEB21)

Elective (Option Area) (2HfB21)

Numerics (BSc21)

Supplementary Module in Mathematics (MEd18)

Please note the registration modalities for the individual proseminars published in the comments to the course catalog: As a rule, places are allocated after pre-registration by e-mail at the preliminary meeting at the end of the lecture period of the summer semester. You must then register online for the examination; the registration period runs from August 1, 2024 to October 9, 2024; if you would like to attend a proseminar but have not been allocated a place, please contact the degree program coordinator immediately.

Seminar: Do, 12-14h, SR 125, Ernst-Zermelo-Str. 1

Preliminary Meeting 15.07., 13 Uhr, SR 403, Ernst-Zermelo-Str. 1

Preparation meetings: Dates by arrangement

Teaching

Teacher: Susanne Knies, Ludwig Striet

Language: in German

Content

Numerous dynamic processes in the natural sciences can be modeled by ordinary differential equations. In this proseminar we will deal with explicit solution methods for differential equations as well as the application situations (reaction kinetics, predator-prey models, mathematical pendulum, different growth processes, . . . ) which can be described by them.

Previous
knowledge

Analysis~I and II, Lineare Algebra~I and II

Usability

Undergraduate Seminar (2HfB21, BSc21, MEH21, MEB21)

Seminar: Mi, 12-14h, SR 125, Ernst-Zermelo-Str. 1

Preregistration:

Preliminary Meeting 16.07., 10: 15 Uhr, Raum 232, Ernst-Zermelo-Str. 1

Preparation meetings: Dates by arrangement

Teaching

Teacher: Angelika Rohde

Assistant: Johannes Brutsche

Language: in German

Content

Paul Erd\H{o}s liked to talk about the *BOOK* in which God keeps the \textit{perfect} proofs of mathematical theorems, according to the famous quote by G. H. Hardy that "there is no permanent place for ugly mathematics" ([1], Preface). In an attempt at a best approximation to this *BOOK*, Aigner and Ziegler have published a large number of sentences with elegant, sophisticated, and sometimes surprising evidence. In this proseminar, a selection of these results will be presented. The spectrum of topics covers all different areas of mathematics, from number theory, geometry, analysis, and combinatorics to graph theory and includes well-known results, such as Littlewood and Offord's lemma, the Dinitz problem, Hilbert's third problem (of his 23 problems presented at the International Congress of Mathematicians in Paris in 1900), the Borsuk conjecture, and many more.

Previous
knowledge

Linear Algebra~I and II, Analysis~I and II

Usability

Undergraduate Seminar (2HfB21, BSc21, MEH21, MEB21)

Seminar: Di, 14-16h, SR 127, Ernst-Zermelo-Str. 1

Preregistration:

Preparation meetings: Dates by arrangement

Teaching

Teacher: Wolfgang Soergel

Assistant: Damian Sercombe

Language: in German

Content

In this proseminar we will discuss topics that are found in various textbooks and scripts for basic lectures in linear algebra but which are not part of the standard material. The lectures build on each other only slightly.

Previous
knowledge

Linear Algebra ~I and II, Analysis~I and II.

Usability

Undergraduate Seminar (2HfB21, BSc21, MEH21, MEB21)

Please note the registration modalities for the individual seminars published in the comments to the course catalog: As a rule, places are allocated after pre-registration by e-mail at the preliminary meeting at the end of the lecture period of the summer semester. You must then register online for the exam; the registration period runs from August 1, 2024 to October 9, 2024.

Seminar

Preregistration:

Preliminary Meeting 19.07., 16 Uhr, Raum 232, Ernst-Zermelo-Str. 1

Preparation meetings: Dates by arrangement

Preregistration:

Preliminary Meeting 19.07., 16 Uhr, Raum 232, Ernst-Zermelo-Str. 1

Preparation meetings: Dates by arrangement

Teaching

Teacher: Ernst August v. Hammerstein

Language: in German

Content

A knot can be mathematically defined relatively simply as a closed curve in the three-dimensional space \(\mathbb{R}^3\). From everyday life, one is certainly already familiar with different types of knots, e.g, surgeon`s knot, sailor`

s knots, and many more. The aim of mathematical knot theory is to find characteristic quantities for the description and classification of knots and thus possibly also to be able to decide whether two knots are equivalent, i.e., if they can be transformed into one another through certain operations.
Ropes, cords or wires can be used to illustrate knots as well as interlacings. Prospective teachers can use these not only in this seminar, but perhaps also later in the classroom to display different results in a very practical way.

Previous
knowledge

Required: Basic Mathematics courses. \ Possibly a little knowledge in topology in addition.

Usability

Supplementary Module in Mathematics (MEd18)

Undergraduate Seminar (2HfB21, BSc21, MEH21, MEB21)

Elective (Option Area) (2HfB21)

Seminar: Fr, 10-12h, SR 125, Ernst-Zermelo-Str. 1

Preregistration:

Preliminary Meeting 18.10.

Preparation meetings: Dates by arrangement

Teaching

Teacher: Thorsten Schmidt

Assistant: Moritz Ritter

Language: Talk/participation possible in German and English

Content

This seminar will focus on theoretical machine learning results, including modern universal approximation theorems, approximation of filtering methods through transformes, application of machine learning methods in financial markets and possibly other related topics. Moreover, we will cover topics in stochastic analysis, like fractional Ito calculus, uncertainty, filtering and optimal transport. You are also invited to suggest related topics.

Previous
knowledge

Required: Basic Probability and either Machine Learning or Probability Theory II (Stochastic Processes).

Usability

Elective (Option Area) (2HfB21)

Mathematical Seminar (BSc21)

Compulsory Elective in Mathematics (BSc21)

Supplementary Module in Mathematics (MEd18)

Mathematical Seminar (MSc14)

Elective (MSc14)

Mathematical Seminar (MScData24)

Elective in Data (MScData24)

Machine-Learning Methods in the Approximation of PDEs

Seminar

Preregistration:

Preliminary Meeting 08.07., 12: 30 Uhr, Büro 209, Hermann-Herder-Str. 10

Preparation meetings: Dates by arrangement

Preregistration:

Preliminary Meeting 08.07., 12: 30 Uhr, Büro 209, Hermann-Herder-Str. 10

Preparation meetings: Dates by arrangement

Teaching

Teacher: Sören Bartels

Assistant: Tatjana Schreiber

Language: Talk/participation possible in German and English

Content

Machine-learning methods have recently been used to approximate solutions of partial differential equations. While in some cases they lead to advantages over classical approaches, their general superiority is widely open. In the seminar we will review the main concepts and recent developments.

Previous
knowledge

Introduction to Theory and Numerics for PDEs

Usability

Elective (Option Area) (2HfB21)

Mathematical Seminar (BSc21)

Compulsory Elective in Mathematics (BSc21)

Supplementary Module in Mathematics (MEd18)

Mathematical Seminar (MSc14)

Elective (MSc14)

Mathematical Seminar (MScData24)

Elective in Data (MScData24)

Medical Data Science

Seminar: Mi, 10-11: 30h, HS Medizinische Biometrie, 1. OG, Stefan-Meier-Str. 26

Preregistration:

Preliminary Meeting 17.07., HS Medizinische Biometrie, 1. OG, Stefan-Meier-Str. 26

Teaching

Teacher: Harald Binder

Language: Talk/participation possible in German and English

Content

To answer complex biomedical questions from large amounts of data, a wide range of analysis tools is often necessary, e.g. deep learning or general machine learning techniques, which is often summarized under the term ``Medical Data Science''. Statistical approaches play an important rôle as the basis for this. A selection of approaches is to be presented in the seminar lectures that are based on recent original work. The exact thematic orientation is still to be determined.

Previous
knowledge

Good knowledge of probability theory and mathematical statistics.

Usability

Elective (Option Area) (2HfB21)

Mathematical Seminar (BSc21)

Compulsory Elective in Mathematics (BSc21)

Supplementary Module in Mathematics (MEd18)

Mathematical Seminar (MSc14)

Elective (MSc14)

Mathematical Seminar (MScData24)

Elective in Data (MScData24)

Seminar: Mi, 16-18h, SR 125, Ernst-Zermelo-Str. 1

Preliminary Meeting 17.07., 16 Uhr

Preparation meetings: Dates by arrangement

Teaching

Teacher: Guofang Wang

Assistant: Xuwen Zhang

Language: Talk/participation possible in German and English

Content

Minimal surfaces are surfaces in space with a “minimal” area and can be described using holomorphic functions. They occur, for example in the investigation of soap skins and the construction of stable objects (e.g. in architecture). In the investigation of minimal surfaces elegant methods from various mathematical fields such as function theory, calculus of variations, differential geometry and partial differential equations. are applied.

Previous
knowledge

Required: Analysis III or knowledge about multidimensional integration and complex analysis. \ Recommended: Elementary knowledge about differential geometry.

Usability

Elective (Option Area) (2HfB21)

Mathematical Seminar (BSc21)

Compulsory Elective in Mathematics (BSc21)

Supplementary Module in Mathematics (MEd18)

Mathematical Seminar (MSc14)

Elective (MSc14)

Elective (MScData24)

Seminar: Di, 14-16h, SR 125, Ernst-Zermelo-Str. 1

Preliminary Meeting 16.07., SR 125, Ernst-Zermelo-Str. 1

Preparation meetings: Dates by arrangement

Teaching

Teacher: Sebastian Goette

Assistant: Mikhail Tëmkin

Language: Talk/participation possible in German and English

Content

We will discuss advanced topics in algebraic topology. Depending on the interest of the participants we could work on one of the following topics---if you have other topic suggestions, please contact the lecturer.

- The Steenrod algebra. An additional structure on the cohomology modulo \(p\) allows finer statements on the existence of continuous mappings, such as the existence of of linearly independent vector fields on spheres. The Wu formulas provide a connection to characteristic classes of manifolds.
- Structured spectra. In order to represent multiplicative (co-)homology functors by spectra, one needs a closed monoidal category of spectra, for example a category of spectra, for example symmetric or orthogonal spectra. In this context we also get to know model structures better.
- \(K\)-theory and index theory. Elliptic differential operators on compact manifolds are manifolds are Fredholm operators. Their index can be defined by the theorem of Atiyah--Singer topologically. We prove this theorem using (mainly) topological methods and give some geometric applications.

Previous
knowledge

Algebraic Topology~I and II

Usability

Elective (Option Area) (2HfB21)

Mathematical Seminar (BSc21)

Compulsory Elective in Mathematics (BSc21)

Supplementary Module in Mathematics (MEd18)

Mathematical Seminar (MSc14)

Elective (MSc14)

Elective (MScData24)

Seminar: Fr, 8-10h, SR 404, Ernst-Zermelo-Str. 1

Preregistration:

Preliminary Meeting 15.07., 11 Uhr, SR 318, Ernst-Zermelo-Str. 1

Preparation meetings: Dates by arrangement

Teaching

Teacher: Annette Huber-Klawitter

Assistant: Xier Ren

Language: Talk/participation possible in German and English

Content

In this seminar, we are going to study finite dimensional (unital, possibly non-commutative) algebras over a (commutative) field \(k\). Prototypes are the rings of square matrices over \(k\), finite field extensions, or the algebra \(k^n\) with diagonal multiplication.

We will concentrate on path algebras of finite quivers (German: Köcher). Modules over them are equivalently described as representations of the quiver. Many algebraic properties can be directly understood from properties of the quiver.

Previous
knowledge

Required: Linear Algebra \ Recommended: Algebra and Number Theory, Commutative Algebra and Introduction to Algebraic Geometry

Usability

Elective (Option Area) (2HfB21)

Mathematical Seminar (BSc21)

Compulsory Elective in Mathematics (BSc21)

Supplementary Module in Mathematics (MEd18)

Mathematical Seminar (MSc14)

Elective (MSc14)

Elective (MScData24)

Within the EUCOR cooperation, you can attend courses at the partner universities. If you click on the universities, you will find links to their course catalogues.

general course catalogue, see https://vorlesungsverzeichnis.unibas.ch/de/semester-planung

Teaching

course catalogue for mathematics see https://www.math.kit.edu/vvz

Teaching

Master Mathématiques Fondamentales et Appliquées see https://irma.math.unistra.fr/linstitut/lmd_enseignement.html#masters

Teaching

Details: please click on the title and follow the link!

Lecture: Mo, 10-12h, 01-009/13, Georges-Köhler-Allee 101

*Course offered by the Faculty of Engineering*

Teaching

Teacher: Thomas Brox

Language: in English

Usability

Elective in Data (MScData24)

Lecture: Di, 10-12h, HS 00-006, Georges-Köhler-Allee 082

*Course offered by the Faculty of Engineering*

Teaching

Teacher: Abhinav Valada

Language: in English

Usability

Elective in Data (MScData24)

Lectures and exercises take place in blocks in individual semester weeks; the exact dates are listed on the course website.

Course offered by the Institute for Economics

Teaching

Teacher: Ekaterina Kazak

Language: in English

Usability

Elective in Data (MScData24)

Lecture: Mo, HS 1098, KG I, Di, 12: 30-14h, HS 1199, KG I

Tutorial: 2 hours, various dates

Course offered by the Institute for Economics

Teaching

Teacher: Roxana Halbleib

Language: in English

Usability

Elective in Data (MScData24)

Lecture: Fr, 8-10h, HS 00-026, Georges-Köhler-Allee 101

*Course offered by the Faculty of Engineering*

Teaching

Teacher: Joschka Boedecker

Language: in English

Usability

Elective in Data (MScData24)

Lecture: Mo, HS 00-026, Georges-Köhler-Allee 101, Mi, 8: 30-10h, HS 00-036, Georges-Köhler-Allee 101

Tutorial: 2 hours, various dates

Course offered by the Faculty of Engineering

Teaching

Teacher: Moritz Diehl

Language: in English

Usability

Elective in Data (MScData24)

Further courses can be admitted as Elective in Data or as Elective after consultation with the Examination Board.

Service Teaching is specifically for students of subjects other than mathematics and not intended for the mathematics degree programmes.

Logic for Computer Science Students

Lecture: Mi, 10-12h, HS 00-026, Georges-Köhler-Allee 101

Tutorial: 2 hours, various dates

Teaching

Teacher: Markus Junker

Assistant: Charlotte Bartnick

Language: in German

Logic for Philosophy Students

Lecture: Mi, 10-12h, HS 3117, KG III

Tutorial: 2 hours, various dates

Teaching

Teacher: Amador Martín Pizarro

Assistant: Stefan Ludwig

Language: in German

Teaching

Teacher: Ernst August v. Hammerstein

Assistant: Hongyi Shen

Language: in English

Lecture: Mo, Mi, 16-18h, HS Rundbau, Albertstr. 21

Tutorial: 2 hours, various dates

Teaching

Teacher: Ernst Kuwert

Assistant: Florian Johne

Language: in German

Mathematics I for Science Students

Lecture: Mo, 14-16h, Fr, 8-10h, HS Rundbau, Albertstr. 21

Tutorial: 2 hours, various dates

Teaching

Teacher: Susanne Knies

Assistant: Sören Andres

Language: in German

Working group seminar: Geometrical Analysis

Di, 16-18h, SR 125, Ernst-Zermelo-Str. 1

Teaching

Teacher: Ernst Kuwert, Guofang Wang

Language: Talk/participation possible in German and English

Working group seminar: Non-Newtonian Fluids

Fr, 10-12h, SR 127, Ernst-Zermelo-Str. 1

Teaching

Teacher: Michael Růžička

Language: Talk/participation possible in German and English

Research seminar: Algebra, Number Theory, and Algebraic Geometry

Fr, 10-12h, SR 404, Ernst-Zermelo-Str. 1

Teaching

Organisation: Annette Huber-Klawitter, Stefan Kebekus, Abhishek Oswal, Wolfgang Soergel

Language: Talk/participation possible in German and English

Research seminar: Applied Mathematics

Di, 14-16h, SR 226, Hermann-Herder-Str. 10

Teaching

Organisation: Sören Bartels, Patrick Dondl, Michael Růžička, Diyora Salimova

Language: Talk/participation possible in German and English

Research seminar: Differential Geometry

Mo, 16-18h, SR 404, Ernst-Zermelo-Str. 1

Teaching

Organisation: Sebastian Goette, Nadine Große

Language: Talk/participation possible in German and English

Research seminar: Mathematical Logic

Di, 14-16h, SR 404, Ernst-Zermelo-Str. 1

Teaching

Organisation: Markus Junker, Amador Martín Pizarro

Language: Talk/participation possible in German and English

Research seminar: Medical Statistics

Mi, 11: 30-13h, HS Medizinische Biometrie, 1. OG, Stefan-Meier-Str. 26

Teaching

Organisation: Harald Binder

Language: Talk/participation possible in German and English

Mi, 16-17h, SR 226, Hermann-Herder-Str. 10

Teaching

Organisation: David Criens, Peter Pfaffelhuber, Angelika Rohde, Thorsten Schmidt

Language: Talk/participation possible in German and English

Mathematics Education Colloquium

Di, 18: 30-20h, HS II, Albertstr. 23b

Teaching

Organisation: Katharina Böcherer-Linder, Ernst Kuwert

Language: in German

Mathematical Colloquium

Do, 15-16h, HS II, Albertstr. 23b

Teaching

Organisation: Nadine Große, Amador Martín Pizarro

Language: Talk/participation possible in German and English

Colloquium for Mathematics Students

Do, 14-15h, HS II, Albertstr. 23b

Teaching

Organisation: Annette Huber-Klawitter, Markus Junker, Amador Martín Pizarro

Language: Talk/participation possible in German and English

Seminar on Data Analysis and Modelling

Fr, 12-13h, SR 404, Ernst-Zermelo-Str. 1

Teaching

Organisation: Harald Binder, Peter Pfaffelhuber, Angelika Rohde, Thorsten Schmidt, Jens Timmer

Language: Talk/participation possible in German and English

Content

Current, interdisciplinary research is presented here, in which mathematical models enable the understanding of natural and social science issues.