Statistical Learning Lab 1

Master's course

The following skills are developed in the course:

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

1st semester: Students have previous knowledge of mathematics/statistics up to 8 ECTS and therefore know simple statistical measures as well as basic statistical test procedures (e.g. t-test). / 2nd semester: No prerequisites / 2nd semester: Module examination MLAL.A1 (Algorithmic 1)

In the lab, the contents of the ILV "Statistical Learning 1" 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

1

Prof. (FH) Dr. Michael Kohlegger

MLAL.2

practice

Compulsory