Data Science & Intelligent Analytics PT
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Machine Learning for Data Science Lab

level of course unit

Master course

Learning outcomes of course unit

Students can compare, assess and independently apply tools for machine learning with respect to their possible deployment in specific problem areas. Furthermore, they are familiar with available options for implementing developed prediction models in a scalable manner and are able to apply these independently.

prerequisites and co-requisites

not specified

course contents

The content of the integrative course “Machine Learning for Data Science” is consol-idated in the lab by means of practical exercises. Acquired knowledge is discussed within the group, which provides profound insights and the consolidation of material that was theoretically discussed in the integrative course.

recommended or required reading

- Bishop, C. (2006) Pattern Recognition and Machine Learning. 1. Auflage, Springer-Verlag, New York (ISBN: 978-0-387-31073-2).
- Géron, A. (2017) Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems. 1. Auflage, O´Reilly, Farnham (ISBN: 978-1491962299).
- McKinney, W. (2017) Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 2. Auflage, O´Reilly, Farnham (ISBN: 978-1491957660).
- Raschka, S.; Mirjalili, V. (2017) Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow. 2. Auflage, Packt Publishing, Birmingham (ISBN: 978-1787125933).
- Shalev-Shwartz, S.; Ben-David, S. (2014) Understanding Machine Learning: From Theory to Algorithms. 1. Auflage, Cambridge University Press, Cambridge (ISBN: 978-1107057135).
- Zheng, A.; Casari, A. (2018) Feature Engineering for Machine Learning Models: Principles and Techniques for Data Scientists. 1. Auflage, O´Reilly, Farnham (ISBN: 978-1491953242).

assessment methods and criteria

-Seminar papers
-Final examination

language of instruction


number of ECTS credits allocated


course-hours-per-week (chw)


planned learning activities and teaching methods

-Lecture with discussion
-Group work
-Performing exercise tasks

semester/trimester when the course unit is delivered


name of lecturer(s)

Prof. (FH) Dr. Kohlegger Michael Demetz Lukas , PhD

year of study


recommended optional program components


course unit code


type of course unit


mode of delivery

In-class course

work placement(s)



Mitarbeiterfoto Karsten Böhm
Prof. (FH) Dipl.-Informatiker Karsten Böhm
Director of Studies
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