Universität Wien

040649 UK Machine Learning (MA) (2024W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
REMOTE
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. 30 participants
Language: German

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 03.10. 11:30 - 13:00 Digital
  • Thursday 10.10. 11:30 - 13:00 Digital
  • Thursday 17.10. 11:30 - 13:00 Digital
  • Thursday 24.10. 11:30 - 13:00 Digital
  • Thursday 31.10. 11:30 - 13:00 Digital
  • Thursday 07.11. 11:30 - 13:00 Digital
  • Thursday 14.11. 11:30 - 13:00 Digital
  • Thursday 21.11. 11:30 - 13:00 Digital
  • Thursday 28.11. 11:30 - 13:00 Digital
  • Thursday 05.12. 11:30 - 13:00 Digital
  • Thursday 12.12. 11:30 - 13:00 Digital
  • Thursday 09.01. 11:30 - 13:00 Digital
  • Thursday 16.01. 11:30 - 13:00 Digital
  • Thursday 23.01. 11:30 - 13:00 Digital
  • Thursday 30.01. 11:30 - 13:00 Digital

Information

Aims, contents and method of the course

Moodle will be used for the course. The course will be presented via a stream.

Basic knowledge about machine learning

Contents:
- ERM classifiers.
-K-NN classifiers.
- Linear classifier and support vector machines
- Principal component analysis (PCA)
- High dimensional statistics & sparsity: Compressed sensing.
- High dimensional statistics & sparsity: Lasso (if time permits).

Assessment and permitted materials

Exercises.

The use of AI tools (e.g. ChatGPT) for the production of texts is only permitted if they are expressly requested by the course leader (e.g. for individual work tasks).

Minimum requirements and assessment criteria

Minimum of 50% is required to pass.

Examination topics

All topics covered in class.

Reading list

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning. Springer Series in Statistics. Springer, New York, second edition, 2009. Data mining, inference, and prediction.

Christophe Giraud. Introduction to high-dimensional statistics, volume 139 of Monographs on Statistics and Applied Probability. CRC Press, Boca Raton, FL, 2015.

Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An introduction to statistical learning, volume 103 of Springer Texts in Statistics. Springer, New York, 2013. With applications in R.

Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, USA, 2014.

Association in the course directory

Last modified: Mo 23.09.2024 14:25