Data Science & Intelligent Analytics PT
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Statistical learning 2 Lab

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

English version available soon

course contents

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

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

The following examination methods are used in the course:

- Project work
- term paper

language of instruction

German

number of ECTS credits allocated

2.5

eLearning quota in percent

0

course-hours-per-week (chw)

1

planned learning activities and teaching methods

The following methods are used:

- Processing of exercises
- Interactive workshop

semester/trimester when the course unit is delivered

2

name of lecturer(s)

Prof. (FH) Dipl.-Informatiker Karsten Böhm

course unit code

MLAL.6

type of course unit

practice

mode of delivery

Compulsory

work placement(s)

none