Universität Wien
Warning! The directory is not yet complete and will be amended until the beginning of the term.

200222 SE Theory and Empirical Research (Mind and Brain) 1 (2022S)

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 1: Scientific Computing in Python for Cognitive Psychology

This course is planned to take place as an in person only course.

If you have no programming/Python background, it is recommended, that you spend a few hours with some introductory Python material (eg: learnpython.org, datacamp) before the start of the course. This will make the first few weeks of the course much easier!

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 07.03. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 09.03. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 14.03. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 16.03. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 21.03. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 23.03. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 28.03. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 30.03. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 04.04. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 06.04. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 25.04. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 27.04. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 02.05. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 04.05. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 09.05. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 11.05. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 16.05. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 18.05. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 23.05. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 25.05. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 30.05. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 01.06. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 08.06. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 13.06. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 15.06. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 20.06. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 22.06. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock
  • Monday 27.06. 13:15 - 14:45 Hörsaal E Psychologie, Liebiggasse 5 1. Stock
  • Wednesday 29.06. 16:45 - 18:15 Hörsaal F Psychologie, Liebiggasse 5 1. Stock

Information

Aims, contents and method of the course

The goal of this course is to introduce students to the use of the Python programming language for solving data analysis problems commonly encountered in psychology research.
The first part of the course will be a general introduction to Python and the most important libraries for data analysis: numpy, scipy, matplotlib, pandas.
The second part of the course will focus on general data science methods (statistical inference with resampling methods, regression models, machine learning).
The final part of the course will apply the previously learned programming to models with special relevance for cognitive science (bayesian model, reinforcement learning, eye-movements).

While the focus of the course will be on the practical and programming aspects, we will also discuss the theoretical apsects of these topics for cognitive science.
Monday classes will focus on theory, with programming tutorials on Wednesdays.

Assessment and permitted materials

Discussion participation and small theory homeworks: 25%
Tutorial participation and coding homeworks: 50%
Final Project: 25%

Minimum requirements and assessment criteria

Active participation in class and programming tutorials.

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

Examination topics

Able to use Python for basic data analysis and visualization tasks

Understands resampling methods for statistical analysis and can implement it in code

Understands the use of random simulations for data analysis

Understands basic linear regression, and how it is related to more advanced regression models

Understands the main concepts of Signal Detection theory

Familiar with the main tools of machine learning

Reading list

Introduction to Modern Statistics (2021): https://openintro-ims.netlify.app/index.html
Think Bayes 2: http://allendowney.github.io/ThinkBayes2/index.html

Gelman, Hill, Vethari (2021): Regression and Other Stories

Statistical Thinking for the 21st Century:
https://statsthinking21.github.io/statsthinking21-core-site/

Ma, Kording, Goldreich: Bayesian models of perception and action

Stanislaw, H., & Todorov, N. (1999). Calculation of signal detection theory measures. Behavior research methods, instruments, & computers, 31(1), 137-149.

Association in the course directory

Last modified: Tu 22.11.2022 08:08