Universität Wien

200114 SE Anwendungsseminar: Geist und Gehirn (2019W)

Introduction to machine learning / Einführung in das maschinelle Lernen

4.00 ECTS (2.00 SWS), SPL 20 - Psychologie
Continuous assessment of course work

Dieses Anwendungsseminar kann für alle Schwerpunkte absolviert werden.

Anwendungsseminare können nur fürs Pflichtmodul B verwendet werden! Eine Verwendung fürs Modul A4 Freie Fächer ist nicht möglich.

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 08.10. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 15.10. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 22.10. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 29.10. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 05.11. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 12.11. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 19.11. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 26.11. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 03.12. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 10.12. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 17.12. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 07.01. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 14.01. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 21.01. 13:15 - 14:45 PCR Computerhörsaal Psychologie, NIG 6.Stock A0607
Tuesday 28.01. 13:15 - 14:45 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 and written answers). 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.

Minimum requirements and assessment criteria

Results of both exams 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