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) Dipl.-Informatiker Karsten Böhm

recommended optional program components


course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)