Start Lehre Weitere Fächer

Weitere Fächer

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

Sekretariat: Angelika Bauder
Tel.: 0621 1703-6302, E-Mail

Institut für Neuropsychologie und 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:

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

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

Institut für Psychopharmakologie


Lehrende:


Veranstaltungshinweise:

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.

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

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

Abteilung Biostatistik


Lehrende:

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

Abteilung Molekularbiologie


Lehrende:


Biochemisches Labor

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

Biochemisches Labor


Lehrende

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

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

Forensische Psychiatrie


Lehrende:

apl. Prof. Dr. Harald Dreßing - LSF / Uni Mannheim

Professor für Theoretische Neurowissenschaften
Abteilungsleiter: Prof. Dr. Daniel Durstewitz
Tel.: 0621 1703-2361, E-Mail

Sekretariat: Christine Roggenkamp, M.A.
Tel.: 0621 1703-6252, E-Mail

Abteilung Theoretische Neurowissenschaften


Lehrende:


Veranstaltungen im Sommersemester 2022

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.


 Veranstaltungen im Sommersemester 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.


Veranstaltungen im Wintersemester 2020/21

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

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.




Zentralinstitut für Seelische Gesundheit (ZI) - https://www.zi-mannheim.de