Machine Learning for Data Science Lab
level of course unit
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
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
language of instruction
number of ECTS credits allocated
planned learning activities and teaching methods
-Lecture with discussion
-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