Statistical learning 2
Niveau
Master's course
Learning outcomes of the courses/module
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 for the course
1st semester: Students have previous knowledge of mathematics/statistics up to 8 ECTS and therefore know simple statistical measures as well as basic statistical test procedures (e.g. t-test). / 2nd semester: No prerequisites / 2nd semester: Module examination MLAL.A1 (Algorithmic 1)
Course content
The following content is discussed in the course:
- Advanced modelling techniques
- Ensemble methods
- Optimization of models
Recommended specialist literature
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
Written exam
Language
German
Number of ECTS credits awarded
6
Share of e-learning in %
33
Semester hours per week
3.0
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Processing of exercises
- Interactive workshop
Semester/trimester in which the course/module is offered
2
Name of lecturer
Prof. (FH) Dr. Lukas Huber
Academic year
Key figure of the course/module
MLAL.5
Type of course/module
integrated lecture
Type of course
Compulsory