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200222 SE Theory and Empirical Research (Mind and Brain) 1 (2021S)
Continuous assessment of course work
Labels
REMOTE
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 We 03.03.2021 11:05 to Fr 05.03.2021 10:59
- Deregistration possible until Fr 05.03.2021 10:59
Details
max. 20 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 17.03. 09:30 - 12:30 Digital
- Wednesday 24.03. 09:30 - 12:30 Digital
- Wednesday 14.04. 09:30 - 12:30 Digital
- Wednesday 21.04. 09:30 - 12:30 Digital
- Wednesday 28.04. 09:30 - 12:30 Digital
- Wednesday 05.05. 09:30 - 12:30 Digital
- Wednesday 12.05. 09:30 - 12:30 Digital
- Wednesday 19.05. 09:30 - 12:30 Digital
- Wednesday 26.05. 09:30 - 12:30 Digital
- Wednesday 02.06. 09:30 - 12:30 Digital
- Wednesday 09.06. 09:30 - 12:30 Digital
- Wednesday 16.06. 09:30 - 12:30 Digital
- Wednesday 23.06. 09:30 - 12:30 Digital
- Wednesday 30.06. 09:30 - 12:30 Digital
Information
Aims, contents and method of the course
Assessment and permitted materials
A multiple-choice exams will be held online, with time limitation, at the end of the seminar. 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
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 exam 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: Fr 12.05.2023 00:19
- 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: Online course. Every lesson of this seminar consists of three parts. The first part is spent on Q&A (online), part two on theory (online videos), whereas the third part is used to expand the theoretical knowledge by practical exercises in python (online jupyter notebooks).