Mathematics for Modeling and Data Analysis

NUMMER: 211047
KÜRZEL: mathModDA
MODULBEAUFTRAGTE:R: Prof. Dr. Laurenz Wiskott
DOZENT:IN: Prof. Dr. Laurenz Wiskott
FAKULTÄT: Fakultät für Informatik
SPRACHE: Englisch
SWS: 4
CREDITS: 5
WORKLOAD: 150 Stunden
ANGEBOTEN IM: jedes Sommersemester

PRÜFUNGEN

FORM: The course is concluded with a digital written exam.
ANMELDUNG:
DATUM: 0000-00-00
BEGINN: 00:00:00
DAUER:
RAUM:

LERNFORM

This course is given with the flipped/inverted classroom concept. First, the students work
through online material by themselves. In the lecture time slot we then discuss the material,
find connections to other topics, ask questions and try to answer them. In the tutorial time
slot the newly acquired knowledge is applied to analytical exercises and thereby deepened. I
encourage all students to work in teams during self-study time as well as in the tutorial.

LERNZIELE

After the successful completion of this course the students
∙ know the material covered in this course, see Content,
∙ do have an intuitive understanding of the basic concepts and can work with that,
∙ can communicate about all this in English.

INHALT

This course covers mathematical methods that are relevant for modeling and data analysis.
Particular emphasis is put on an intuitive understanding as is required for a creative command
of mathematics. The following topics are covered:
∙ Functions and how to visualize them
∙ Vector spaces
∙ Matrices as transformations
∙ Systems of linear differential equations
∙ Qualitative analysis of nonlinear differential equations
∙ Bayesian theory
∙ Markov chains

VORAUSSETZUNGEN CREDITS

Bestandene Modulabschlussprüfung

EMPFOHLENE VORKENNTNISSE

Basic knowledge of calculus and linear algebra.

AKTUELLE INFORMATIONEN

Verwendung des Moduls (in anderen Studiengängen):
B.Sc. Physik, M.Sc. Cognitive Science
Stellenwert der Note für die Endnote: 5 / 170
(Im Studiengang werden Module im Umfang von 170 CP benotet und 10 CP nicht benotet)

SONSTIGE INFORMATIONEN

There is a lecture, which provides the content, and a tutorial, where you solve exercises and
can deepen your understanding of the content. The exercises are solved in the tutorial in a
group effort, not at home, which is the reason why it takes 3 hours rather than the usual 1.5
hours.