Statistical learning 2 Lab
level of course unit
Learning outcomes of course unit
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.
prerequisites and co-requisites
English version available soon
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.
recommended or required reading
- 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)
- 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)
assessment methods and criteria
The following examination methods are used in the course:
- Project work
- term paper
language of instruction
number of ECTS credits allocated
eLearning quota in percent
planned learning activities and teaching methods
The following methods are used:
- Processing of exercises
- Interactive workshop
semester/trimester when the course unit is delivered
name of lecturer(s)
Prof. (FH) Dr. Lukas Huber, Prof. (FH) Dr. Michael Kohlegger
course unit code
type of course unit
mode of delivery