Preliminary course catalogue - changes and additions are still possible.
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New (and partly not yet in den annotated course catalogue):
Precourse (for students in mathematics)
Language: in German
30.09.–02.10. and 04.10.; begins on 30.09. at 9h15 in HS Rundbau.
Precourse (for students in science and engineering)
Lecturer: Susanne Knies
Language: in German
02.10.–05.10.2024, begins at 9h in HS Rundbau.
Exercising the Basics
Lecturer: Fachschaft
Language: in German
Supervised Exercising
Lecturer: Fachschaft
Language: in German
Analysis I
Lecturer: Ernst Kuwert
Assistant: Xuwen Zhang
Language: in German
Lecture: Di, Mi, 8-10h, HS Rundbau, Albertstr. 21
Tutorial: 2 hours, various dates
Sit-in exam: date to be announced
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.
Required: High school mathematics. \
Attendance of the preliminary course (for students in mathematics) is recommended.
Analysis (2HfB21, BSc21, MEH21, MEB21)
Analysis I (BScInfo19, BScPhys20)
Linear Algebra I
Lecturer: Sebastian Goette
Assistant: Mikhail Tëmkin
Language: in German
Lecture: Mo, Do, 8-10h, HS Rundbau, Albertstr. 21
Tutorial: 2 hours, various dates
Sit-in exam: date to be announced
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.
Required: High school mathematics. \
Attendance of the preliminary course (for students in mathematics) is recommended.
Linear Algebra (2HfB21, BSc21, MEH21)
Linear Algebra (MEB21)
Linear Algebra I (BScInfo19, BScPhys20)
Numerics I
Lecturer: Patrick Dondl
Assistant: Jonathan Brugger
Language: in German
Lecture: Mi, 14-16h, HS Weismann-Haus, Albertstr. 21a
Tutorial: 2 hours, every other week, various dates
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.
Required: Linear Algebra~I \
Recommended: Linear Algebra~II and Analysis~I (required for Numerics~II)
Numerics (BSc21)
Numerics (2HfB21, MEH21)
Numerics I (MEB21)
Elementary Probability Theory I
Lecturer: Thorsten Schmidt
Assistant: Simone Pavarana
Language: in German
Lecture: Fr, 10-12h, HS Weismann-Haus, Albertstr. 21a
Tutorial: 2 hours, every other week, various dates
Sit-in exam: date to be announced
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).
Required: Linear Algebra I, Analysis I and II. \
Note that Linear Algebra I can be attended in parallel.
Elementary Probabilty Theory (2HfB21, MEH21)
Elementary Probability Theory I (BSc21, MEB21, MEdual24)
Further Chapters in Analysis
Lecturer: Ernst August v. Hammerstein
Language: in German
Lecture: Mi, 8-10h, HS Weismann-Haus, Albertstr. 21a
Tutorial: 2 hours, various dates
Sit-in exam: date to be announced
This compulsory lecture for teacher training students in the M.Ed. builds on the basic lectures Analysis I and II and supplements them with the following two main topics:
\textit{Multidimensional integration:} The one-dimensional Riemann integral known from Analysis I is generalized to real-valued functions of several variables, for which a suitable instrument for measuring the content/volume of multidimensional sets is first introduced with the Jordan content. Then the classical integral theorems (transformation theorem, Fubini's theorem) are derived, and path and surface integrals are considered. With the help of the divergence and rotation of vector fields, the two aforementioned integral types can be related to each other using the integral theorems of Gauß and Stokes, which considerably simplifies the calculations in practical applications.
\textit{Complex Analysis:} In contrast to Analysis I, here the (complex) differentiability of functions of a complex variable is examined. As will be shown, complex differentiable, so-called holomorphic functions are subject to much stricter rules and laws than their real-valued counterparts, which leads to both beautiful and surprising results. To this end, we will prove Cauchy's intergal theorem and Cauchy's integral formula and take a closer look at applications and conclusions from these.
Analysis~I and II, Linear Algebra~I and II
Further Chapters in Analysis (MEd18, MEH21, MEdual24)
Basics in Applied Mathematics
Lecturer: Sören Bartels, Moritz Diehl, Thorsten Schmidt
Language: in English
Lecture: Di, Do, 8-10h, SR 404, Ernst-Zermelo-Str. 1
Tutorial: 2 hours, date to be determined
Programming exercise: 2 hours, date to be determined
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.
None that go beyond admission to the degree programme.
Basics in Applied Mathematics (MScData24)
Lecturer: Wolfgang Soergel
Assistant: Damian Sercombe
Language: in German
Lecture: Di, Do, 10-12h, HS Weismann-Haus, Albertstr. 21a
Tutorial: 2 hours, various dates
Sit-in exam: date to be announced
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.
Linear Algebra I and II
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)
Lecturer: Maximilian Stegemeyer
Language: in German
Lecture: Di, Do, 10-12h, SR 404, Ernst-Zermelo-Str. 1
Tutorial: 2 hours, date to be determined
Algebraic topology studies topological spaces by assigning algebraic objects, e.g. groups, vector spaces or rings, to them in a particular way. This assignment is usually done in a way which is invariant under homotopy equivalences. Therefore one often speaks of homotopy invariants and algebraic topology can be seen as the study of the construction and the properties of homotopy invariants.
In this lecture we will first recall the notion of the fundamental group of a space and study its connection to covering spaces. Then we will introduce the singular homology of a topological space and study it extensively. In the end, we will consider cohomology and homotopy groups and explore their relation to singular homology. We will also consider various applications of these invariants to topological and geometric problems.
Topology
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)
Analysis III
Lecturer: Michael Růžička
Assistant: Luciano Sciaraffia
Language: in German
Lecture: Mo, Mi, 10-12h, HS Weismann-Haus, Albertstr. 21a
Tutorial: 2 hours, various dates
Sit-in exam: date to be announced
The Analysis III lecture deals with measure and integration theory, with particular emphasis on the Lebesgue measure. These theories are of particular importance for many further lectures in analysis, applied mathematics, stochastics, probability theory and geometry, as well as physics. Main topics are measures and integrals in \(\mathbb R^n\), Lebesgue spaces, convergence theorems, the transformation theorem, surface integrals and Gauss' integral theorem.
Required: Analysis I and II, Linear Algebra I \
Useful: Linear Algebra II
Elective (Option Area) (2HfB21)
Analysis III (BSc21)
Mathematical Concentration (MEd18, MEH21)
Elective in Data (MScData24)
Complex Analysis
Lecturer: Stefan Kebekus
Assistant: Andreas Demleitner
Language: in German
Lecture: Di, Do, 8-10h, HS II, Albertstr. 23b
Tutorial: 2 hours, date to be determined
Sit-in exam: date to be announced
Analysis I and II, Linear Algebra I
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Mathematical Concentration (MEd18, MEH21)
Pure Mathematics (MSc14)
Elective (MSc14)
Elective (MScData24)
Introduction to Theory and Numerics of Partial Differential Equations
Lecturer: Patrick Dondl
Assistant: Ludwig Striet, Oliver Suchan
Language: in English
Lecture: Mo, Mi, 12-14h, HS II, Albertstr. 23b
Tutorial: 2 hours, date to be determined
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.
Required: Analysis~I and II, Linear Algebra~I and II as well as knowledge about higher-dimensional integration (e.g. from Analysis~III or from Further Chapters in Analysis) \
Recommended: Numerics for differential equations, Functional analysis
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)
Mathematical Statistics
Lecturer: Ernst August v. Hammerstein
Language: in English
Lecture: Di, Do, 14-16h, SR 404, Ernst-Zermelo-Str. 1
Tutorial: 2 hours, date to be determined
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.
Probability Theory (in particular measure theory and conditional probabilities/expectations)
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)
Model Theory
Lecturer: Amador Martín Pizarro
Assistant: Charlotte Bartnick
Language: in English
Lecture: Di, Do, 12-14h, SR 404, Ernst-Zermelo-Str. 1
The lecture will probably be held in English.
In this course the basics of geometric model theory will be discussed and concepts such as quantifier elimination and categoricity will be introduced. A theory has quantifier elimination if every formula is equivalent to a quantifier-free formula. For the theory of algebraically closed fields of fixed characteristic, this is equivalent to requiring that the projection of a Zariski-constructible set is again Zariski-constructible. A theory is called \(\aleph_1\)-categorical if all the models of cardinality \(\aleph_1\) are isomorphic. A typical example is the theory of non-trivial \(\mathbb Q\)-vector spaces. The goal of the course is to understand the theorems of Baldwin-Lachlan and of Morley to characterize \(\aleph_1\)-categorical theories.
necessary: Mathematical Logic \
useful: Algebra and Number Theory
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Pure Mathematics (MSc14)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Elective (MScData24)
Probabilistic Machine Learning
Lecturer: Giuseppe Genovese
Language: in English
Lecture: Di, Do, 12-14h, HS II, Albertstr. 23b
Tutorial: 2 hours, date to be determined
The goal of the course is to provide a mathematical treatment of deep neural networks and energy models, that are the building blocks of many modern machine learning architectures. About neural networks we will study the basics of statistical learning theory, the back-propagation algorithm and stochastic gradient descent, the benefits of depth. About energy models we will cover some of the most used learning and sampling algorithms. In the exercise classes, besides solving theoretical problems, there will be some Python programming sessions to implement the models introduced in the lectures.
Probability Theory I \
Basic knowledge of Markov chains is useful for some part of the course.
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)
Probability Theory II – Stochastic Processes
Lecturer: Angelika Rohde
Assistant: Johannes Brutsche
Language: in English
Lecture: Mo, Mi, 14-16h, HS II, Albertstr. 23b
Tutorial: 2 hours, date to be determined
A stochastic process \((X_t)_{t\in T}\) is a family of random variables, where mostly the situation \(T = \mathbb{N}\) or \(T = [0, 1]\) is studied. Basic examples include stationary time series, the Poisson process and Brownian motion as well as processes derived from those. The lecture includes ergodic theory and its applications, Brownian motion and especially the study of its path properties, the elegant concept of weak convergence on Polish spaces as well as functional limit theorems. Finally, we introduce stochastic integration with respect to local martingales, based on the continuous time version of the martingale transform.
Probability Theory I
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)
Calculus of Variations
Lecturer: Guofang Wang
Assistant: Florian Johne
Language: in German
Lecture: Mo, Mi, 10-12h, HS II, Albertstr. 23b
Tutorial: 2 hours, date to be determined
The aim of the calculus of variations is to minimise or maximise certain mathematically treatable quantities. More precisely, we consider \(\Omega \subset {\mathbb R}^n\) functionals or variation integrals of the form \[F (u) = \int_\Omega f(x,u (x ),Du (x))dx, \quad \hbox{ f\"ur } u : \Omega\to {\mathbb R}\] on \(\Omega \subset {\mathbb R}^n\).
Examples are arc length and area, as well as energies of fields in physics. The central question is the existence of minimisers. After a brief introduction to the functional analysis tools, we will first familiarise ourselves with some necessary and sufficient conditions for the existence of minimisers. We will see that compactness plays a very important role. We will then introduce some techniques that help us to get by without compactness in special cases: The so-called compensated compactness and the concentrated compactness.
necessary: Functional Analysis \
useful: PDE, numerical PDE
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)
Reading courses
Lecturer: All professors and 'Privatdozenten' of the Mathematical Institute
Language: Talk/participation possible in German and English
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.
Reading Course (MEd18, MEH21)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Futures and Options
Lecturer: Eva Lütkebohmert-Holtz
Language: in English
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.
Elementary Probability Theory I
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)
Linear Algebraic Groups
Lecturer: Abhishek Oswal
Assistant: Damian Sercombe
Language: in English
Lecture: Mo, 14-16h, SR 125, Ernst-Zermelo-Str. 1
There is no information available yet.
There is no information available yet.
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Pure Mathematics (MSc14)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Elective (MScData24)
Machine Learning and Mathematical Logic
Lecturer: Maxwell Levine
Language: in English
Lecture: Do, 14-16h, SR 226, Hermann-Herder-Str. 10
Tutorial: 2 hours, date to be determined
Developments in artificial intelligence have boomed in recent years, holding the potential to reshape not just our daily routines but also society at large. Many bold claims have been made regarding the power and reach of AI. From a mathematical perspective, one is led to ask: What are its limitations? To what extent does our knowledge of reasoning systems in general apply to AI?
This course is intended to provide some applications of mathematical logic to the field of machine learning, a field within artificial intelligence. The goal of the course is to present a breadth of approachable examples.
The course will include a gentle introduction to machine learning in a somewhat abstract setting, including the notions of PAC learning and VC dimension. Connections to set theory and computability theory will be explored through statements in machine learning that are provably undecidable. We will also study some applications of model theory to machine learning.
The literature indicated in the announcement is representative but tentative. A continuously written PDF of course notes will be the main resource for students.
Background in basic mathematical logic is strongly recommended. Students should be familiar with the following notions: ordinals, cardinals, transfinite induction, the axioms of ZFC, the notion of a computable function, computable and computably enumerable sets (a.k.a. recursive and recursively enumerable sets), the notions of languages and theories and structures as understood in model theory, atomic diagrams, elementarity, and types. The concepts will be reviewed briefly in the lectures. Students are not expected to be familiar with the notion of forcing in set theory.
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Pure Mathematics (MSc14)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Elective in Data (MScData24)
Markov Chains
Lecturer: David Criens
Language: in English
Lecture: Mi, 10-12h, SR 226, Hermann-Herder-Str. 10
Tutorial: 2 hours, date to be determined
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.
Required: Elementary Probability Theory I \
Recommended: Analysis III, Probability Theory I
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)
Mathematical Introduction to Deep Neural Networks
Lecturer: Diyora Salimova
Assistant: Ilkhom Mukhammadiev
Language: in English
Lecture: Mi, 12-14h, SR 226, Hermann-Herder-Str. 10
Tutorial: 2 hours, date to be determined
The course will provide an introduction to deep learning algorithms with a focus on the mathematical understanding of the objects and methods used. Essential components of deep learning algorithms will be reviewed, including different neural network architectures and optimization algorithms. The course will cover theoretical aspects of deep learning algorithms, including their approximation capabilities, optimization theory, and error analysis.
Analysis I and II, Lineare Algebra I and II
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)
Measure Theory
Lecturer: Peter Pfaffelhuber
Assistant: Samuel Adeosun
Language: in English
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.
Basic courses in analysis, and an understanding of mathematical proofs.
Elective in Data (MScData24)
Numerical Optimal Control
Lecturer: Moritz Diehl
Language: in English
Tutorial / flipped classroom: Di, 14-16h, HS II, Albertstr. 23b
Sit-in exam: date to be announced
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:
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.
Required: Analysis I and II, Linear Algebra I and II \
Recommended: Numerics I, Ordinary Differential Equations, Numerical Optimization
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)
Theory and Numerics for Partial Differential Equations – Selected Nonlinear Problems
Lecturer: Sören Bartels
Assistant: Tatjana Schreiber
Language: in English
Lecture: Mo, 12-14h, SR 226, Hermann-Herder-Str. 10
Tutorial: 2 hours, date to be determined
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 and total-variation regularized minimization problems. 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.
'Introduction to Theory and Numerics for PDEs' or 'Introduction to PDEs'
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)
Topics in Mathematical Physics
Lecturer: Chiara Saffirio
Language: in English
Lecture: Mo, 12-14h, SR 404, Ernst-Zermelo-Str. 1
This course provides an introduction to analytical methods in mathematical physics, with a particular emphasis on many-body quantum mechanics. A central focus is the rigorous proof of the stability of matter for Coulomb systems, such as atoms and molecules. The key question - why macroscopic objects made of charged particles do not collapse under electromagnetic forces - remained unresolved in classical physics and lacked even a heuristic explanation in early quantum theory. Remarkably, the proof of stability of matter marked the first time that mathematics offered a definitive answer to a fundamental physical and stands as one of the early triumphs of quantum mechanics.
Content:
Analysis III and Linear Algebra are required. \
No prior knowledge of physics is assumed; all relevant physical concepts will be introduced from scratch.
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Pure Mathematics (MSc14)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Elective (MScData24)
Topological Data Analysis
Lecturer: Mikhail Tëmkin
Language: in English
Information will follow!
Information will follow!
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Pure Mathematics (MSc14)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Elective in Data (MScData24)
Introduction to Mathematics Education
Lecturer: Katharina Böcherer-Linder
Language: in German
Mo, 10-12h, SR 226, Hermann-Herder-Str. 10
Tutorial: 2 hours, date to be determined
Sit-in exam: date to be announced
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.
Required: Basics lectures (Analysis, Linear Algebra)
The course ‘Introduction to Mathematics Education’ is therefore recommended from the 4th semester at the earliest.
(Introduction to) Mathematics Education (2HfB21, MEH21, MEB21)
Introduction to Mathematics Education (MEdual24)
Mathematics Education ‒ Functions and Analysis
Lecturer: Katharina Böcherer-Linder
Language: in German
Do, 9-12h, SR 226, Hermann-Herder-Str. 10
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.
Introduction to Mathematics Education \
Knowledge about analysis and numerics
Mathematics Education for Specific Areas of Mathematics (MEd18, MEH21, MEB21)
Mathematics Education ‒ Probability Theory and Algebra
Lecturer: Frank Reinhold
Language: in German
Mi, 11-14h, SR 404, Ernst-Zermelo-Str. 1
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.
Introduction to Mathematics Education \ knowledge from stochastics and algebra
Mathematics Education for Specific Areas of Mathematics (MEd18, MEH21, MEB21)
Mathematics education seminar: Media Use in Teaching Mathematics
Lecturer: Jürgen Kury
Language: in German
Seminar: Mi, 15-18h, SR 404, Ernst-Zermelo-Str. 1
Recommended: Basic courses in mathematics
GeoGebra Account (can be created in the seminar)
Supplementary Module in Mathematics Education (MEd18, MEH21, MEB21)
Mathematics education seminars at Freiburg University of Education
Lecturer: Lecturers of the University of Education Freiburg
Language: in German
Supplementary Module in Mathematics Education (MEd18, MEH21, MEB21)
Module "Research in Mathematics Education"
Lecturer: Lecturers of the University of Education Freiburg
Language: in German
Part 1: Seminar 'Development Research in Mathematics Education ‒ Selected Topics': Mo, 14-16h, Raum noch nicht bekannt, PH Freiburg
Part 2: Seminar 'Research Methods in Mathematics Education': Mo, 16-19h, Raum noch nicht bekannt, PH Freiburg
Part 3: Master's thesis seminar: Development and Optimisation of a Research Project in Mathematics Education
Registration: see course descriptions
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.
Research in Mathematics Education (MEd18, MEH21, MEB21)
Learning by Teaching
Organisation: Susanne Knies
Language: in German
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.
Elective (Option Area) (2HfB21)
Elective (BSc21)
Elective (MSc14)
Elective (MScData24)
Supplementary Module in Mathematics (MEd18)
School Mathematical Aspects of Analysis and Linear Algebra
Lecturer: Katharina Böcherer-Linder, Markus Junker
Language: in German
Mo, 14-16h, SR 404, Ernst-Zermelo-Str. 1
Supplementary Module in Mathematics (MEd18)
High-school Oriented Aspects of Analysis and Linear Algebra (MEdual24)
Computer exercises for 'Introduction to Theory and Numerics of Partial Differential Equations'
Lecturer: Patrick Dondl
Language: in English
The computer tutorial accompanies the lecture with programming exercises.
See the lecture – additionally: programming knowledge.
Elective (Option Area) (2HfB21)
Elective (BSc21)
Supplementary Module in Mathematics (MEd18)
Elective (MSc14)
Elective (MScData24)
Computer exercises in Numerics
Lecturer: Patrick Dondl
Assistant: Jonathan Brugger
Language: in German
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.
See the lecture {\em Numerics I} (which should be attended in parallel or should already have been completed). \ Additionally: Elementary programming knowledge.
Computer Exercise (2HfB21, MEH21, MEB21)
Elective (Option Area) (2HfB21)
Numerics (BSc21)
Supplementary Module in Mathematics (MEd18)
Computer exercises for 'Theory and Numerics of Partial Differential Equations – Selected Nonlinear Problems'
Lecturer: Sören Bartels
Assistant: Tatjana Schreiber
Language: in English
In the practical exercises accompanying the lecture 'Theory and Numerics for Partial Differential Equations – Selected Nonlinear Problems', the algorithms developed and analyzed in the lecture are implemented and tested experimentally. The implementation can be carried out in the programming languages Matlab, C++ or Python. Elementary programming knowledge is assumed.
see lecture
Elective (Option Area) (2HfB21)
Elective (BSc21)
Supplementary Module in Mathematics (MEd18)
Elective (MSc14)
Elective (MScData24)
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, 2025 to October 8, 2025; if you would like to attend a proseminar but have not been allocated a place, please contact the degree program coordinator immediately.
Lecturer: Annette Huber-Klawitter
Assistant: Christoph Brackenhofer
Language: in German
Seminar: Mi, 8-10h, SR 404, Ernst-Zermelo-Str. 1
Preregistration: Entry in list with Mr Backenhofer, room 437
Preliminary Meeting 24.07., 13:00, SR 404, Ernst-Zermelo-Str. 1
Preparation meetings for talks: Dates by arrangement
Students training to become teachers are given priority.
Number theory is concerned with questions about the properties of integers. Many of them can be easily formulated but their solutions require heavy mathematical machinery. In this proseminar we want to get to know number-theoretic problems that have elementary solutions. Topics include divisibility properties of integers, continued fractions and transcendental numbers.
Analysis I,II, Linear Algebra I, II
Undergraduate Seminar (2HfB21, BSc21, MEH21, MEB21)
Undergraduate seminar: Ordinary Differential Equations
Lecturer: Diyora Salimova
Language: Talk/participation possible in German and English
Seminar: Mi, 14-16h, SR 226, Hermann-Herder-Str. 10
Preregistration: until 10 July 2025 per email to Diyora Salimova
Preliminary Meeting 15.07., 11:00, SR 226, Hermann-Herder-Str. 10
Preparation meetings for talks: Dates by arrangement
In this proseminar we will explore several aspects of Ordinary Differential Equations (ODEs), a fundamental area of mathematics with widespread applications across natural sciences, engineering, economics, and beyond. Students will engage actively by presenting and discussing various topics, including existence and uniqueness theorems, stability analysis, linear systems, nonlinear dynamics, and numerical methods for solving ODEs. Participants will enhance their analytical skills and deepen their theoretical understanding by studying classical problems and contemporary research directions.
Analysis I and II, Linear Algebra I and II
Undergraduate Seminar (2HfB21, BSc21, MEH21, MEB21)
Lecturer: Heike Mildenberger
Assistant: Stefan Ludwig
Language: in German
Seminar: Di, 16-18h, SR 127, Ernst-Zermelo-Str. 1
Preregistration: no preregistration
Preliminary Meeting 23.07., Fakultätssitzungsraum 427, Ernst-Zermelo-Str. 1
Preparation meetings for talks: Dates by arrangement
The topics are: Finite and infinite graphs, Eulerian paths, connectivity properties, colourings, spanning trees, random graphs. If desired, more advanced subjects, such as the Rado graph and 0-1 laws or probabilistic methods, can also be presented.
Linear Algebra I and II, Analysis I and II
Undergraduate Seminar (2HfB21, BSc21, MEH21, MEB21)
Proseminar: Mathematik im Alltag
Lecturer: Susanne Knies
Language: in German
Remaining places in the M.Ed. seminar after the school practical semester can be allocated as undergraduate seminar places. For more information see there!
Undergraduate Seminar (2HfB21, BSc21, MEH21, MEB21)
Supplementary Module in Mathematics (MEd18)
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, 2025 to October 8, 2025.
Lecturer: Susanne Knies
Assistant: Jonah Reuß
Language: in German
The seminar is preferably intended for M.Ed. students. Remaining places can be allocated as undergraduate seminar places.
Undergraduate Seminar (2HfB21, BSc21, MEH21, MEB21)
Supplementary Module in Mathematics (MEd18)
Seminar: Computational PDEs – Gradient Flows and Descent Methods
Lecturer: Sören Bartels
Language: Talk/participation possible in German and English
Seminar: Mo, 14-16h, SR 226, Hermann-Herder-Str. 10
Preliminary Meeting 15.07., 12:30, Raum 209, Hermann-Herder-Str. 10
Preparation meetings for talks: Dates by arrangement
The seminar will be devoted to the development of reliable and efficient discretizations of time stepping methods for parabolic evolution problems. The considered model problems either result from minimization problems or dynamical systems and are typically constrained or nondifferentiable. Criteria that allow to adjust the step sizes and strategies that lead to an acceleration of the convergence to stationary configurations will be addressed in the seminar. Specific topics and literature will be assigned in the preliminary meeting.
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)
Lecturer: Wolfgang Soergel
Language: Talk/participation possible in German and English
Seminar: Di, 14-16h, SR 127, Ernst-Zermelo-Str. 1
Preregistration: In case of interest, please email to Wolfgang Soergel
Preliminary Meeting 17.07., 12:15
Structure of noncommutative rings with applications to representations of finite groups.
necessary: Linear Algebra I and II \
useful: Algebra and Number Theory
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: Medical Data Science
Lecturer: Harald Binder
Language: Talk/participation possible in German and English
Seminar: Mi, 10:15-11:30h, HS Medizinische Biometrie, 1. OG, Stefan-Meier-Str. 26
Preregistration:
Preliminary Meeting 23.07., 10:15, HS Medizinische Biometrie, 1. OG, Stefan-Meier-Str. 26
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.
Good knowledge of probability theory and mathematical statistics.
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: Minimal Surfaces
Lecturer: Guofang Wang
Language: Talk/participation possible in German and English
Seminar: Mi, 16-18h, SR 125, Ernst-Zermelo-Str. 1
Preliminary Meeting 30.07., SR 125, Ernst-Zermelo-Str. 1
Preparation meetings for talks: Dates by arrangement
Minimal surfaces are surfaces in space with a ‘minimal’ area and can be described using holomorphic functions. They appear, for example, in the investigation of soap skins and the construction of stable objects (e.g. in architecture). Elegant methods from various mathematical fields such as complex analysis, calculus of variations, differential geometry, and partial differential equations are used to analyse minimal surfaces.
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: Random Walks
Lecturer: Angelika Rohde
Assistant: Johannes Brutsche
Language: Talk/participation possible in German and English
Seminar: Mo, 16-18h, SR 127, Ernst-Zermelo-Str. 1
Preliminary Meeting 22.07., Raum 232, Ernst-Zermelo-Str. 1
Preparation meetings for talks: Dates by arrangement
Preregistration: If your are interested in the seminar, please write an email to Johannes Brutsche listing your prerequisites in probability and note if you plan to attend the Probability Theory II.
Random walks are stochastic processes (in discrete time) formed by successive summation of independent, identically distributed random variables and are one of the most studied topics in probability theory. Many results that are part of this seminar also carry over to Brownian motion and related processes in continuous time. In particular, the theory for random walks contains many central and elegant proof ideas which can be extended to various other settings. We start the theory at the very beginning but quickly move on to proving local central limit theorems, study Green's function and recurrence properties, hitting times and the Gambler's ruin estimate. Further topics may include a dyadic coupling with Brownian motion, Dirichlet problems, random walks that are not indexed in \(\mathbb{N}\) but the lattice \(\mathbb{Z}^d\), and intersection probabilities for multidimensional random walks (which are processes \(X:\mathbb{N}\rightarrow\mathbb{R}^d\)). Here, we will see that in dimension \(d=1,2,3\) two paths hit each other with positive probability, while for \(d\geq 4\) they avoid each other almost surely.
Probability Theory I \
Some talks only require knowledge of Stochastics I, so if you are interested in the seminar and have not taken part in the probability theory I class, do not hesitate to reach out to us regarding a suitable topic.
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: Data-Driven Medicine from Routine Data
Lecturer: Nadine Binder
Note: Only for the degree programme "Mathematics in Data and Technology"
Imagine being able to use routine data such as diagnoses, lab results, and medication plans to answer medical questions in innovative ways and improve patient care. In this seminar, we will learn to identify relevant data, understand suitable analysis methods, and what to consider when applying them in practice. Together, we will analyze scientific studies on routine data and discuss clinical questions, the methods used, and their feasibility for implementation.
What makes this seminar special: Medical and mathematics students collaborate to understand scientific studies from both perspectives. When possible, you will work in pairs (or individually if no pair can be formed) to analyze a study from your respective viewpoints and prepare related presentations. You may test available programming code or develop your own approaches to replicate the methods and apply them to your own questions. The pairs can be formed during the preliminary meeting.
necessary: Basics in Applied Mathematics \
useful: Probability Theory I
Mathematical Seminar (MScData24)
Elective in Data (MScData24)
Graduate Student Speaker Series
Organisation: Sören Bartels, Ernst August v. Hammerstein
Language: in English
Seminar: Mi, 14-16h, SR 127, Ernst-Zermelo-Str. 1
In the Graduate Student Speaker Series, students of the M.Sc. degreee programme ‘Mathematics in Data and Technology’ talk about their Master's thesis or their programming projects, and the lecturers of the programme talk about their fields of work.
Graduate Student Speaker Series (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.
University of Basel
general course catalogue, see https://vorlesungsverzeichnis.unibas.ch/de/semester-planung
Karlsruhe Institute for Technology
course catalogue for mathematics see https://www.math.kit.edu/vvz
University of Strasbourg
Master Mathématiques Fondamentales et Appliquées see https://irma.math.unistra.fr/linstitut/lmd_enseignement.html#masters
Service Teaching is specifically for students of subjects other than mathematics and not intended for the mathematics degree programmes.
Logic for Computer Science Students
Lecturer: Heike Mildenberger
Language: in German
Lecture: Mi, 10-12h, HS 00-026, Georges-Köhler-Allee 101
Tutorial: 2 hours, various dates
Logic for Philosophy Students
Lecturer: Markus Junker
Assistant: Stefan Ludwig
Language: in German
Lecture: Mi, 10-12h, HS 3043, KG III
Tutorial: 2 hours, various dates
Mathematical Methods for Economics and Finance
Lecturer: Ernst August v. Hammerstein
Language: in English
Lecture: Di, 10-12h, HS 1221, KG I
Exercise session: Fr, 10-12h, -, -
Mathematics I for Computer Science and Engineering Students
Lecturer: Peter Pfaffelhuber
Language: in German
Lecture: Mo, Di, 12-14h, HS Rundbau, Albertstr. 21
Tutorial: 2 hours, various dates
Mathematics I for Science Students
Lecturer: Susanne Knies
Assistant: Ben Snodgrass
Language: in German
Lecture: Mo, 14-16h, HS Rundbau, Albertstr. 21, Fr, 8-10h, HS Rundbau, Albertstr. 21
Tutorial: 2 hours, various dates
Working group seminar: Geometrical Analysis
Lecturer: Ernst Kuwert, Guofang Wang
Language: Talk/participation possible in German and English
Di, 16-18h, SR 404, Ernst-Zermelo-Str. 1
Working group seminar: Non-Newtonian Fluids
Lecturer: Michael Růžička
Language: Talk/participation possible in German and English
Fr, 10-12h, SR 127, Ernst-Zermelo-Str. 1
Research seminar: Algebra, Number Theory, and Algebraic Geometry
Organisation: Annette Huber-Klawitter, Stefan Kebekus, Abhishek Oswal, Wolfgang Soergel
Language: Talk/participation possible in German and English
Fr, 10-12h, SR 404, Ernst-Zermelo-Str. 1
The research seminar consists of research talks from the respective specialisation area. The single talks are usually announced in the weekly programme and on the homepage.
Research seminar: Applied Mathematics
Organisation: Sören Bartels, Patrick Dondl, Michael Růžička, Diyora Salimova
Language: Talk/participation possible in German and English
Di, 14-16h, SR 226, Hermann-Herder-Str. 10
The research seminar consists of research talks from the respective specialisation area. The single talks are usually announced in the weekly programme and on the homepage.
Research seminar: Differential Geometry
Organisation: Sebastian Goette
Language: Talk/participation possible in German and English
Mo, 16-18h, SR 404, Ernst-Zermelo-Str. 1
The research seminar consists of research talks from the respective specialisation area. The single talks are usually announced in the weekly programme and on the homepage.
Research seminar: Mathematical Logic
Organisation: Amador Martín Pizarro, Heike Mildenberger
Language: Talk/participation possible in German and English
Di, 14-16h, SR 125, Ernst-Zermelo-Str. 1
The research seminar consists of research talks from the respective specialisation area. The single talks are usually announced in the weekly programme and on the homepage.
Research seminar: Medical Statistics
Organisation: Harald Binder
Language: Talk/participation possible in German and English
Mi, 13-14h, HS Medizinische Biometrie, 1. OG, Stefan-Meier-Str. 26
Research seminar: Probability Theory
Organisation: David Criens, Peter Pfaffelhuber, Angelika Rohde, Thorsten Schmidt
Language: Talk/participation possible in German and English
Mi, 16-17h, SR 226, Hermann-Herder-Str. 10
The research seminar consists of research talks from the respective specialisation area. The single talks are usually announced in the weekly programme and on the homepage.
Mathematics Education Colloquium
Lecturer: Various speakers
Organisation: Katharina Böcherer-Linder, Ernst Kuwert
Language: in German
Di, 18:30-20h, HS II, Albertstr. 23b
The Mathematics Education Colloquium aims to show concrete examples, to further develop existing concepts and to encourage didactic experimentation. It is aimed at teachers of all school types, students, trainee teachers and anyone interested.
Mathematical Colloquium
Lecturer: Various speakers
Organisation: Amador Martín Pizarro
Language: Talk/participation possible in German and English
Do, 15-16h, HS II, Albertstr. 23b
Colloquium for Mathematics Students
Lecturer: Various speakers
Organisation: Annette Huber-Klawitter, Markus Junker, Amador Martín Pizarro
Language: Talk/participation possible in German and English
Do, 14-15h, HS II, Albertstr. 23b
In the ‘Mathematical Colloquium for Students’, topics from the various fields of work are presented in a ‘mathematically understandable’ way. The lectures are aimed at advanced Bachelor's students, Master's students of all specialisations, as well as doctoral students. Afterwards, there will be an opportunity for questions, discussion and dialogue over coffee and tea.
Seminar on Data Analysis and Modelling
Lecturer: Various speakers
Organisation: Harald Binder, Peter Pfaffelhuber, Angelika Rohde, Thorsten Schmidt, Jens Timmer
Language: Talk/participation possible in German and English
Fr, 12-13h, SR 404, Ernst-Zermelo-Str. 1
Current, interdisciplinary research is presented here, in which mathematical models enable the understanding of natural and social science issues.