Machine Learning for Data Science
level of course unit
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
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
planned learning activities and teaching methods
-Lecture with discussion
-Performing exercise tasks
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