Data Science & Intelligent Analytics PT
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Statistical learning 1

level of course unit

Master's course

Learning outcomes of course unit

The following skills are developed in the course:

- Students are familiar with the functionality of basic algorithms in the field of data science.
- Students understand the statistical concepts and working methods behind the algorithms covered.
- Students are able to select suitable algorithms for given problems.
- Students are familiar with the data structures, runtime specifics and complexity classes required by the algorithms covered.
- Students can apply the algorithms in isolated problems.

prerequisites and co-requisites

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)

course contents

The following content is discussed in the course:

- Statistical measures (point and interval estimators)
- Statistical test procedures
- Grouping algorithms (classification trees, agglomerative hierarchical clustering, etc.)
- Regression algorithms (regression trees, random forests, etc.)
- Associative algorithms
- Procedures for preprocessing data (e.g. principal component analysis)

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

Written exam

language of instruction


number of ECTS credits allocated


eLearning quota in percent


course-hours-per-week (chw)


planned learning activities and teaching methods

The following methods are used:

- Lecture with discussion
- Processing of exercises
- Interactive workshop

semester/trimester when the course unit is delivered


name of lecturer(s)

Prof. (FH) Dr. Johannes Lüthi, Prof. (FH) Dr. Michael Kohlegger

recommended optional program components


course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)