Module


General information
Mathematics and Multivariate Statistics
Mathematics and Multivariate Statistics
MADS-MMS
MathMultivar-01-MA-M
Prof. Dr. Doerfel, Stephan (stephan.doerfel@haw-kiel.de)
Prof. Dr. Doerfel, Stephan (stephan.doerfel@haw-kiel.de)
Wintersemester 2021/22
1 Semester
In der Regel jedes Semester
Englisch
Curricular relevance (according to examination regulations)
Study Subject Study Specialization Study Focus Module type Semester
M.Sc. - DS - Data Science Pflichtmodul

Qualification outcome
Areas of Competence: Knowledge and Understanding; Use, application and generation of knowledge; Communication and cooperation; Scientific self-understanding / professionalism.
Students know
- fundamental statistical concepts and methods relevant for modern data science and understand for which type of tasks they are most suitable.
- the connection between the covered statistical methods and algorithms and their mathematics foundations.
Students are able to
- apply statistical methods to real-world problems.
- reflect on advantages and limitations of algorithms in practical terms
- derive insights and build on the related scientific literature
Students are able to
- correctly interpret and communicate the approach and results both in technical and functional terms
- work professionally with standard data mining methodology.
Content information
Statistics:
- Clustering
- Frequent Itemset Mining
- Dimensionality Reduction

Mathematics:
- Basic linear algebra and calculus
- Similarity and distance measures
- Matrix decomposition techniques
- Gradient descent
- Lecture Slides
- Additional Literature:
- Leskovec, Rajaraman and Ullman: Mining of Massive Datasets. Cambridge Univeristy Press; third edition. Available online: BLOCKEDmmds[.]orgBLOCKED
- Boyd and Vandenberghe: Introduction to Applied Linear Algebra. Cambridge University Press. Available online: https://web.stanford.edu/~boyd/vmls/vmls.pdf
- Raschka and Mirjalili: Python Machine Learning. Packt (2017).
Teaching formats of the courses
Teaching format SWS
Lehrvortrag + Übung 4
Workload
4 SWS
5,0 Credits
48 Hours
102 Hours
Module Examination
Method of Examination Duration Weighting wird angerechnet gem. § 11 Absatz 2 PVO Graded Remark
Portfolioprüfung 100 %