Universität Wien

200149 SE Theory and Empirical Research (Mind and Brain) 1 (2022W)

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

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

  • Wednesday 05.10. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 12.10. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 19.10. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 09.11. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 16.11. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 23.11. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 30.11. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 07.12. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 14.12. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 11.01. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 18.01. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Wednesday 25.01. 09:45 - 13:00 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607

Information

Aims, contents and method of the course

In the brain & mind specialization, we offer independent TEWA 1s and TEWA 2s. TEWA 1s are generally more focused on 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 and are held by different lecturers.

Each unit of TEWA I machine learning consists of two parts. Part 1 teaches machine learning theory, with emphasis on models and their evaluation. Part 2 teaches practical application based on scikit-learn in Python. The following topics will be covered:
key terms, history, connection to psychology, supervised learning problem, linear regression, decision trees, model complexity, train-test-split, cross-validation, bias-variance-tradeoff, random forests, curse of dimensionality, model interpretation, classification, lda, svm, real world examples

Assessment and permitted materials

There are two ways to get assessment points:
1.) A maximum of 20 points via an online exam (multiple-choice and free questions) 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 you encounter technical problems, you are required to report them immediately during the exam time (email or moodle forum). Complains after the exam time cannot be considered.
2.) A maximum of 15 points can be obtained from exercises and the final project (First exercise: 3 points, exercises 2-10: 1 point per exercise, project: 3 points). One exercise will be handed out each unit. The full solution of the exercise must be uploaded in moodle until before the next unit. All students who have handed in an exercise will receive the point. From these students, some will be randomly selected and must answer questions to their solutions. If it becomes obvious that the exercise has not been understood, 4 points will be withdrawn. The same applies to the project.

Minimum requirements and assessment criteria

30 points are considered as 100% (35 points achievable). Percentage of achieved points to 30 gives the grade: >50% is necessary for a positive end result (min. 16 points). >50% to 63% (16 to 18 points): grade 4, >63% to 75% (19 to 22 points): grade 3, >75% to 88% (23 to 26 points): grade 2, >88% (from 27 points): 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

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

Association in the course directory

Last modified: Mo 19.09.2022 15:08