Additional Subjects

Information for students

For Psychology students

Institut Neuropsychologie und Klinsche Psychologie

Wissenschaftliche Direktorin: Prof. Dr. Herta Flor
Tel.: 0621 1703-6302, E-Mail

Sekretariat: Angelika Bauder
Tel.: 0621 1703-6302, E-Mail
Institut Neuropsychologie und Klinische Psychologie

Abteilung Klinische Psychologie

Leitung: Prof. Dr. Peter Kirsch
Tel.: 0621 1703-6501, -6511, E-Mail

Sekretariat: Ellen Schmucker
Tel.: 0621 1703-6502, E-Mail
Abteilung Klinische Psychologie

Lehrende / Instructors:


/ Date

/ Instructor

Thema / Download
/ Topic / Download

WS 2017/18

For Pharmacology students

Institut für Psychopharmakologie

Wissenschaftlicher Direktor: Prof. Dr. Rainer Spanagel
Tel.: 0621 1703-6251, E-Mail

Sekretariat: Christine Roggenkamp
Tel.: 0621 1703-6252, E-Mail
Institut für Psychopharmakologie



Aktuelle Informationen zum Seminar für Psychopharmakologie finden Sie auf der Seite Veranstaltungen. / For up-to-date information about the Psychopharmacology Seminar, please visit the Events page.

For Statistics students

Abteilung Biostatistik

Kommissarische Leitung: Prof. Dr. Stefan Wellek
Tel.: 0621 1703-6001, E-Mail

Sekretariat: Mireille Lukas
Tel.: 0621 1703-6002, E-Mail
Abteilung Biostatistik


For Biological Sciences students

Abteilung Molekularbiologie

Leitung: Prof. Dr. Dusan Bartsch
Tel.: 0621 1703-6202, E-Mail

Abteilung Molekularbiologie


Biochemisches Labor

Leitung: apl. Prof. Dr. Patrick Schloss
Tel.: 0621 1703-2901, E-Mail
Biochemisches Labor


For Law students

Forensische Psychiatrie

Leitung: apl. Prof. Dr. Harald Dreßing
Tel.: 0621 1703-2941, E-Mail

Sekretariat: Martina Herbig
Tel.: 0621 1703-2381, E-Mail
Forensische Psychiatrie


For Physics, Mathematics and Computer science students

Physics, Mathematics and Computer Science

Professor in Theoretical Neuroscience /
Department Head: Prof. Dr. Daniel Durstewitz
Phone: +49 (0)621 1703-2361, e-mail

Secretariat: Christine Roggenkamp, M.A.
Phone: +49 (0)621 1703-6252, e-mail

Theoretical Neuroscience


Event notes:

Up-to-date information about the lecture and its contents you will find here:

Time Series Analysis and Recurrent Neural Networks (MVSpec)

Lecture (2 hrs): Wed 11.00-13.00
Exercises (2 hrs): Wed 14.00-16.00
Location: INF 227, SR 2.403

This course will deal primarily with modelbased analysis of time series, that is with insights and predictions that could be gained by inferring a mathematical model of the dynamical process from the observed data. It will cover state of the art methods from the fields of computational statistics, machine learning & AI, and nonlinear dynamics. Starting from simple linear autoregressive models, we will advance to nonlinear dynamical systems, state space approaches, and generative deep recurrent neural networks. The latter class of models is particularly interesting and powerful, as it can –after being trained on time series data –generate new instances of the observed system’s behavior on its own, e.g. new samples of text written in a certain style, or new trajectories from an observed dynamical system.

In the practical part of the course, in addition to theoretical exercises, you will analyze time series data yourself (and you are free to suggest or bring data along yourself), using provided or simple selfwritten Python or Matlabbased code (prerequisite is familiarity with at least  one of these two languages, as well as some basics in statistics).

Time series are ubiquitous in nature and social networks, and provide a very rich source of information about the underlying system. Examples include anything from stock markets and financial data, sun spot emissions, weather forecasts, GPS tracking and other mobile/wearable sensor readings, healthcare and epidemiological data, to behavioral data like website‐visit histories, choice behavior, spoken language, or recordings from the brain like functional neuroimaging or electrophysiological data. General aims of time series analysis reach from predicting the future or generating forecasts, to a thorough scientific understanding of the underlying dynamical system that generated the observed series.