200114 SE Anwendungsseminar: Geist und Gehirn (2019W)
Introduction to machine learning / Einführung in das maschinelle Lernen
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 02.09.2019 11:00 to We 25.09.2019 09:00
- Deregistration possible until Fr 04.10.2019 09:00
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
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
- 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
- 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.