Data Science & Intelligent Analytics PT
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Machine Learning for Data Science

level of course unit

Master course

Learning outcomes of course unit

Students are familiar with tools (e.g. libraries, cloud platforms or software tools) that support machine learning and are able to compare these tools in regard to their suit-ability for specific problem areas. Furthermore, they are familiar with available options for implementing developed prediction models in a scalable manner (big data).

prerequisites and co-requisites

Algorithmics & Statistics 1
Software Development

course contents

Students acquire applied knowledge in the area of machine learning within the context of the course, thus building on the content of the courses Algorithmics & Statistics 1 and Software Development 1. In this course students deal with the implementation of previously theoretically learned algorithms and associated specific technological support. The course specifically includes the following topics:

-Applied machine learning, e.g. with scikit-learn, Theano, Pylearn2, NuPIC (Python) or rpart, randomForest, party, gbm, kernlab, e1071 (R) or Rattle, RapidMiner (click-based software)
-Applied deep learning, e.g. with tensorflow (Python) or nnet (R) or Neuroph Studio (click-based software)
-Processing machine learning problems with cloud infrastructures, e.g. Azure Machine Learning Studio (Microsoft) or Machine Learning Web Services (Amazon)

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

Final examination or seminar paper

language of instruction


number of ECTS credits allocated


course-hours-per-week (chw)


planned learning activities and teaching methods

-Lecture with discussion
-Performing exercise tasks
-Interactive workshop

semester/trimester when the course unit is delivered


name of lecturer(s)

Despotovic Miroslav , MA

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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