Introduction to statistical modelling and machine learning.
Learning objective
The goal is to familiarise students with the principles and tools of machine learning, and to enable them to apply them for practical data analysis.
Content
multivariate probability distributions; comparison of distributions; regression; classification; model selection and cross-validation; clustering and density estimation; mixture models; neural networks
Literature
C. Bishop: Pattern Recognition and Machine Learning, Springer 2006 T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer 2017 R. Duda, P. Hart, D. Stork: Pattern Classification, Wiley 2000
Performance assessment
Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
In examination block for
Bachelor's Degree Programme in Geospatial Engineering 2018; Version 06.10.2021 (Examination Block 2)
The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examination
written 90 minutes
Additional information on mode of examination
Die Note setzt sich wie folgt zusammen: i) 90min schriftliche Prüfung und ii) obligatorisches Leistungselement in Form von Übungsaufgaben während des Semesters. Hinweis: Die Schlussnote setzt sich zu 70% aus der Prüfungsnote und zu 30% aus der Bewertung der Übungsaufgaben zusammen. Weder die Pruefung noch das obligatorische Leistungselement muessen für sich allein bestanden werden.
Written aids
None
If the course unit is part of an examination block, the credits are allocated for the successful completion of the whole block. This information can be updated until the beginning of the semester; information on the examination timetable is binding.
Learning materials
No public learning materials available.
Only public learning materials are listed.
Groups
No information on groups available.
Restrictions
Places
40 at the most
Priority
Registration for the course unit is until 26.02.2021 only possible for the primary target group
Performance assessment as a semester course