040649 UK Machine Learning (2020W)
Continuous assessment of course work
Labels
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 14.09.2020 09:00 to We 23.09.2020 12:00
- Registration is open from Mo 28.09.2020 09:00 to We 30.09.2020 12:00
- Deregistration possible until Sa 31.10.2020 12:00
Details
max. 30 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 07.10. 09:45 - 11:15 Digital
- Wednesday 14.10. 09:45 - 11:15 Digital
- Wednesday 21.10. 09:45 - 11:15 Digital
- Wednesday 28.10. 09:45 - 11:15 Digital
- Wednesday 04.11. 09:45 - 11:15 Digital
- Wednesday 11.11. 09:45 - 11:15 Digital
- Wednesday 18.11. 09:45 - 11:15 Digital
- Wednesday 25.11. 09:45 - 11:15 Digital
- Wednesday 02.12. 09:45 - 11:15 Digital
- Wednesday 09.12. 09:45 - 11:15 Digital
- Wednesday 16.12. 09:45 - 11:15 Digital
- Wednesday 13.01. 09:45 - 11:15 Digital
- Wednesday 20.01. 09:45 - 11:15 Digital
- Thursday 28.01. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
Oral exam.
Minimum requirements and assessment criteria
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.
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: Fr 12.05.2023 00:12
- 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.