Modul


Allgemeine Informationen
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
Studiengänge und Art des Moduls (gemäß Prüfungsordnung)
Studiengang Vertiefungsrichtung Schwerpunkt Modulart Fachsemester
M.Sc. - DS - Data Science Pflichtmodul

Kompetenzen / Lernergebnisse
Kompetenzbereiche: Wissen und Verstehen; Einsatz, Anwendung und Erzeugung von Wissen; Kommunikation und Kooperation; Wissenschaftliches Selbstverständnis/Professionalität.
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.
Angaben zum Inhalt
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).
Lehrformen der Lehrveranstaltungen
Lehrform SWS
Lehrvortrag + Übung 4
Arbeitsaufwand
4 SWS
5,0 Leistungspunkte
48 Stunden
102 Stunden
Modulprüfung
Prüfungsform Dauer Gewichtung wird angerechnet gem. § 11 Absatz 2 PVO Benotet Anmerkung
Portfolioprüfung 100 %