Universität Wien

200177 SE Theory and Empirical Research (Mind and Brain) 2 (2023S)

8.00 ECTS (4.00 SWS), SPL 20 - Psychologie
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

TEWA 2: Advanced data science in python: bayesian modeling and deep learning

Teaching language: English
This course is planned to take place as an in person only course.

Previous experience with Python programming is necessary.

In the brain & mind specialization, we offer TEWA 1s and TEWA 2s. TEWA 1s are generally focused on more computational aspects/theory, and TEWA 2s are more hands-on use of specific data collection techniques. During your Master's studies, you will need to attend one TEWA 1 and one TEWA 2. You should first attend a TEWA 1, and then a TEWA 2.

  • Monday 06.03. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 20.03. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 27.03. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 17.04. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 24.04. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 08.05. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 15.05. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 22.05. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 05.06. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 12.06. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 19.06. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Monday 26.06. 13:15 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607

Information

Aims, contents and method of the course

Most of the courses is focused on teaching theoretical and practical aspects of Bayesian modeling for data analysis. A special focus will be analyzing data from psychological experiments using probabilistic programming methods. The final part of the course introduces deep learning, with a focus on computer vision applications and their link to studying the cognitive neuroscience of vision.
Learning outcome: data analysis with probabilistic programming (PyMC & ArViz libraries), bayesian workflow, bayesian models of cognition, bayesian brains, basic deep learning (keras), deep learning as a model of the brain.

Course format:
theory classes: presentations - journal club style discussions
practical classes: python programming tutorials & data analysis projects

Assessment and permitted materials


Assessment: presentations, small theory homework questions, participation in the discussions, participation in the tutorials, data analysis project.

Minimum requirements and assessment criteria

Active participation in class and programming exercises.

1: >87%
2: 76 - 87%
3: 64 - 75%
4: 51 - 63%
5: <=50%

Examination topics


Able to use Python for advanced data analysis and visualization tasks on real world data

Understands the use of data simulations for data analysis

Familiar with main concepts of Bayesian modeling (prior, likelihood, posterior)

linear and generalized regression models, hierarchical models, and how to implement their frequentist and Bayesian versions in Python

familiar with main deep learning architectures, optimization techniques

deep learning library tensorflow/keras

Reading list

Downey, A. B. (2021). Think Bayes. " O'Reilly Media, Inc.".

Martin, O. A., Kumar, R., & Lao, J. (2022). Bayesian Modeling and Computation in Python. Chapman and Hall/CRC.

Martin, O. (2018). Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ. Packt Publishing Ltd.

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC.

Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press.

Yang, G. R., & Wang, X. J. (2020). Artificial neural networks for neuroscientists: a primer. Neuron, 107(6), 1048-1070.

Goodfellow, I. (2016). Nips 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160.

Association in the course directory

Last modified: Su 05.03.2023 15:28