040649 UK Machine Learning (MA) (2022W)
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 Mo 12.09.2022 09:00 to Fr 23.09.2022 12:00
- Deregistration possible until Sa 15.10.2022 23:59
Details
max. 30 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
Thursday
06.10.
09:45 - 11:15
Digital
Thursday
13.10.
09:45 - 11:15
Digital
Thursday
20.10.
09:45 - 11:15
Digital
Thursday
27.10.
09:45 - 11:15
Digital
Thursday
03.11.
09:45 - 11:15
Digital
Thursday
10.11.
09:45 - 11:15
Digital
Thursday
17.11.
09:45 - 11:15
Digital
Thursday
24.11.
09:45 - 11:15
Digital
Thursday
01.12.
09:45 - 11:15
Digital
Thursday
15.12.
09:45 - 11:15
Digital
Thursday
12.01.
09:45 - 11:15
Digital
Thursday
19.01.
09:45 - 11:15
Digital
Thursday
26.01.
09:45 - 11:15
Digital
Information
Aims, contents and method of the course
Assessment and permitted materials
Exercises.
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.
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: Th 11.05.2023 11:27
- 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.