Universität Wien

200188 SE Theory and Empirical Research (Mind and Brain) 1 (2022S)

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

Diese LV kann für alle Schwerpunkte absolviert werden.
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

In general, it is assumed that the appointments will take place on site. However, due to the unstable pandemic situation, we may have to switch to online.

  • Wednesday 09.03. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 16.03. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 23.03. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 30.03. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 06.04. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 27.04. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 04.05. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 11.05. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 18.05. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 25.05. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 01.06. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 08.06. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 15.06. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 22.06. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 29.06. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607

Information

Aims, contents and method of the course

Generally, 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, whereas in the brain & mind specialization TEWAs are dependent on each other.

Aims and contents: Upon successful completion, students will have knowledge about:
- historical outline of machine-learning development
- key terms of the field (AI, ML, ...)
- important concepts (bias-variance trade off, cross-validation, ...)
- overview of important algorithms in the field
- basic programming in python
- application of ML algorithms to real-world data
Methods: On-site course. Every lesson of this course consists of two parts. The first part is spent on theory (lecture), whereas the second part is used to expand the theoretical knowledge by practical exercises in python (jupyter notebooks).

Assessment and permitted materials

There are two options to get assessment points:
- A maximum of 20 points via an online multiple-choice test at the end of the seminar, which is subject to time constraints. Students need a computer (PC, laptop, tablet, etc.) and an internet connection for this. If there are any technical problems, they are to be reported immediately (email or moodle forum). Complains after the exam time cannot be considered.
- Maximum 12 points from exercises (1 point per assessed exercise). One exercise will be hand out each unit. The full solution of the exercise has to be uploaded in moodle until before the next unit. All students who have handed in an exercise will receive one point. From these students one will be randomly selected and has to present the full solution. If it becomes obvious that the exercise has not been solved independently, 4 points will be withdrawn, which means that a very good can no longer be achieved in this course.

Minimum requirements and assessment criteria

A maximum of 32 point can be achieved. Percentage of achieved points >50% is necessary for a positive end result. >50% to 63%: grade 4, >63% to 75%: grade 3, >75% to 88%: grade 2, >88%: grade 1

Examination topics

All topics covered in the seminar are relevant for the exams. The exams will ask for topics of the theoretical and the practical part.

Reading list

- An Introduction to Statistical Learning, Free download from: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf
- The Elements of Statistical Learning, Free download from: https://web.stanford.edu/~hastie/Papers/ESLII.pdf

Association in the course directory

Last modified: Th 03.03.2022 15:48