Additional Subjects
Scientific Director: Prof. Dr. Herta Flor
Phone: +49 621 1703-6302, e-mail
Secretariat: Angelika Bauder
Phone: +49 621 1703-6302, e-mail
Institute of Cognitive and Clinical Neuroscience
Abteilung Klinische Psychologie
Head: Prof. Dr. Peter Kirsch
Phone: +49 621 1703-6501, -6511, e-mail
Secretariat: Ellen Schmucker
Phone: +49 621 1703-6502, e-mail
Department of Clinical Psychology
Instructors:
Scientific Director: Prof. Dr. Rainer Spanagel
Phone: +49 621 1703-6251, e-mail
Secretariat: Christine Roggenkamp
Phone: +49 621 1703-6252, E-Mail
Institute for Psychopharmacology
Instructors:
Event notes:
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.
Professor in Theoretical Neuroscience
Department Head: Prof. Dr. Daniel Durstewitz
Phone: +49 621 1703-2361, e-mail
Secretariat: Christine Roggenkamp, M.A.
Phone: +49 621 1703-6252, e-mail
Department Theoretical Neuroscience
Lecturers:
Event notes:
Courses in the summer semester 2021
MVSpec lecture ‘Time Series Analysis & Recurrent Neural Networks’
- Time: Wed 11.00‐13.00 (lecture), Wed 14.00 – 16.00 (exercises)
Location: INF 227 SR 2 403 - Lecturers: D. Durstewitz, N.N.
This course will deal primarily with model‐based 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 auto‐regressive models, we will advance to nonlineardynamical 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.
Up-to-date information about the lecture (winter term 19-20) and its contents you will find here:
MVSpec lecture ,Time Series Analysis & Recurrent Neural Networks’
- Time: Wed 11.00‐13.00 (lecture), Wed 14.00 – 16.00 (exercises)
- Location: INF 227 SR 2 403
- Lecturers: D. Durstewitz, G. Koppe
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.
This course will deal primarily with model‐based 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 auto‐regressive 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 self‐written Python‐ or Matlab‐based code (prerequisite is familiarity with at least one of these two languages, as well as some basics in statistics).
Zentralinstitut für Seelische Gesundheit (ZI) - https://www.zi-mannheim.de