Data Science & Intelligent Analytics PT
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Statistical learning 2

level of course unit

Master's course

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

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:

- Advanced modelling techniques
- Ensemble methods
- Optimization of models

recommended or required reading

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)

assessment methods and criteria

Written exam

language of instruction

German

number of ECTS credits allocated

6

eLearning quota in percent

33

course-hours-per-week (chw)

3

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

2

course unit code

MLAL.5

type of course unit

integrated lecture

mode of delivery

Compulsory