Universität Wien

200113 SE Theory and Empirical Research (Mind and Brain) 1 (2020S)

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

  • Tuesday 10.03. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 10.03. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 17.03. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 17.03. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 24.03. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 24.03. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 31.03. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 31.03. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 21.04. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 21.04. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 28.04. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 28.04. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 05.05. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 05.05. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 12.05. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 12.05. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 19.05. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 19.05. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 26.05. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 26.05. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 09.06. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 09.06. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 16.06. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 16.06. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 23.06. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 23.06. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 30.06. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
  • Tuesday 30.06. 15:00 - 16:30 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607

Information

Aims, contents and method of the course

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
Every lesson of this seminar consists of two parts. The first half is spent on theory, whereas the second half is used to expand the theoretical knowledge by practical exercises in python.

Assessment and permitted materials

Two written exams (multiple choice). One after the first half of the seminar, the second at the end. Both count equally in terms of achievable points. No tools are allowed. Furthermore, one can earn a point for each exercise. The point is given at the end of the exercise for trying to solve the exercise or during the exercise time, when the exercise is successfully solved. This points also rise the achievable points for calculating the grade.
Update:
1. The bonus point system is not applicable during the learning at home time, all students get extra points.
2. The multiple-choice exams will be held online, with time limitation. Students need a computer (PC, Laptop, Tablet, etc.) and an internet connection. If you encounter technical problems, you need to report them immediately (email or moodle forum). Reports after the end of the exam cannot be considered.

Minimum requirements and assessment criteria

Results of both exams and the exercise points will be summed up. Total 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. Both 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: Mo 07.09.2020 15:21