Statistical Learning Lab 2

Master's course

The following skills are developed in the course:

- Students can practically understand advanced algorithms of data science.
- Students can configure advanced algorithms of data science for specific purposes.
- Students can apply the algorithms in isolated problems.

Module examination MLAL.A1 (Algorithmik 1)

In the lab, the contents of the ILV "Statistical Learning 2" are advanced with the aid of practical exercises. The knowledge gained will be discussed in the group and thus allow a deep insight into the material and consolidation of the knowledge, which was theoretically dealt with in the ILV.

PRIMARY LITERATURE:
- Murphy, K. P. (2012): Machine Learning: A Probabilistic Perspective (Ed. 1), MIT Press, Cambridge (ISBN: 978-0-262-01802-9)
- Bishop, C. (2006): Pattern Recognition and Machine Learning (Ed. 1), Springer-Verlag, New York (ISBN: 978-0-387-31073-2)

SECONDARY LITERATURE:
- James, G.; Witten, D; Hastie, T.; Tibshirani, R. (2013): An Introduction to Statistical Learning: with Applications in R (Ed. 1), Springer Science and Business Media, New York (ISBN: 978-1-461-471387)
- Steele, B.; Chandler, J.; Reddy, S. (2016): Algorithms for Data Science (Ed. 1), Springer, Berlin (ISBN: 978-3319457956)

The following examination methods are used in the course:

- Project work
- term paper

English

2.5

30

1.0

The following methods are used:

- Processing of exercises
- Interactive workshop

2

Prof. (FH) Dr. Lukas Huber, Prof. (FH) Dr. Michael Kohlegger

MLAL.6

practice

Compulsory

none