The Zurich Computational Psychiatry Course 2018 will take place from Sep 10th – 14th.

Duration
4 days + 1 day (Practical Sessions)

Date
10th – 14th September 2018

Registration Start
February 2018

Registration Ends
August 2018

Designed for
Master Students, PhDs, PostDocs, Clinicians and anyone deeply interested in Computational Psychiatry

ECTS Points
3 (only for members of the University of Zurich and ETH)

Course Fee
500 CHF for the main course
(free for students of the ETH and University of Zurich)
50 CHF per Practical Session

Requirements
Basic programming skills
(e.g. Matlab or Python)
Basic knowledge of statistics

The course will consist of five parts:

The first day covers topics in Clinical Psychiatry providing a conceptual basis for the type of questions that Computational Psychiatry will need to address, including an open panel discussions led by scientists with a clinical background in Psychiatry.

The second day explains basic modelling principles, Bayesian Inference (Bayesian Models of Perception, Bayesian Hierarchical Learning, Predictive Coding), Machine Learning & the Drift-Diffusion Model

The third day includes models of planning and decision making (MDPs, POMDPs, Reinforcement Learning, Active Inference), Biophysical Models (DCMs) and more advanced model inversion techniques (model selection, model averaging, MCMC, Variational Bayes)

The fourth day features a series of talks by leading scientists on the applications of Computational Psychiatry, including a panel discussion on the future of Computational Psychiatry.

The fifth day is optional and includes hands-on practical programming sessions for a subset of the presented models (IMPORTANT NOTICE: Seats for the practical sessions need to be booked separately and are limited!!!)

  • Practical Session A: Bayesian Learning using the Hierarchical Gaussian Filter
  • Practical Session B: Active inference using the Active Inference Toolbox
  • Practical Session C: Model Inversion using the Variational Bayes Toolbox
  • Practical Session D: Reinforcement Learning using the hBayesDM Package
Zurich Computational Psychiatry Course Sep 2018

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