312.240 (18S) Selected Topics in Statistics
Overview
- Lecturer
- Course title german Ausgewählte Kapitel der Statistik
- Type Lecture - Practical class (continuous assessment course )
- Hours per Week 3.0
- ECTS credits 5.0
- Registrations 8 (25 max.)
- Organisational unit
- Language of instruction no language of instruction was specified
- Course begins on 16.03.2018
Time and place
Course Information
Teaching methodology including the use of eLearning tools
- Deutscher Vortrag mit englischen Folien.
- Teilweise Blended Learning.
- Aktive Teilnahme der Studierenden, studentische Präsentationen zu ausgewählten statistischen Themen (aus Büchern, siehe unten), Ausarbeitung von Übungsaufgaben (teilweise R-basiert).
Course content
Regression and classification methods based on the classical linear model and its extensions constitute a core part of multivariate statistics. Over the last two decades various new approaches have been developed which are more appropriate for those data we currently collect in business intelligence (BI), information technology (IT), and biotechnology applications. Most of them relax parametric model assumptions and add additional flexibility when our task is fitting models to quantitative observations. Others are destined to perform predictive tasks. The latter belong to the group of statistical learning procedures. A methodological and computational challenge is the huge size (‘big data’) and the high complexity of many data sets of interest.
Modern regression and classification techniques heavily rely on efficient computing. Statistical learning blends concepts of statistics and machine learning. In the lectures selected statistical approaches and appropriate computer concepts are introduced. A deeper understanding can be gained in exercises applying the open source statistics and graphics environment R.
Literature
- Bühlmann, P. et al. (eds; 2016) Handbook of Big Data. Boca Raton: CRC Press.
- Efron, B. and Hastie, T. (2016) Computer Age Statistical Inference. Cambridge: Cambridge University Press.
- Gareth, J., Witten, D., Hastie, T. and Tibshirani, R (2013) An Introduction to Statistical Learning with Applications in R. New York: Springer.
- Hastie, T., Tibshirani, R. and Friedman, J. (2009; 2nd edition) The Elements of Statistical Learning. Data Mining, Inference, and Prediction. New York: Springer.
- Hastie, T., Tibshirani, R. and Wainwright, M. (2015) Statistical Learning with Sparsity. Boca Raton: CRC Press.
- Schimek, M. G. (ed; 2000) Smoothing and Regression. Approaches, Computation and Application. New York: John Wiley.
Examination information
Grading scheme
Grade / Grade grading schemePosition in the curriculum
- Thematic Doctoral Programme Modeling-Analysis-Optimization of discrete, continuous and stochastic systems
(SKZ: ---, Version: 16W.1)
-
Subject: Modeling-Analysis-Optimization of discrete, continuous and stochastic systems
(Compulsory subject)
-
Modeling-Analysis - Optimization of discrete, continuous and stochastic systems (
0.0h XX / 0.0 ECTS)
- 312.240 Selected Topics in Statistics (3.0h VU / 5.0 ECTS)
-
Modeling-Analysis - Optimization of discrete, continuous and stochastic systems (
0.0h XX / 0.0 ECTS)
-
Subject: Modeling-Analysis-Optimization of discrete, continuous and stochastic systems
(Compulsory subject)
- Master's degree programme Information Management
(SKZ: 922, Version: 13W.2)
-
Subject: Freie Wahlfächer
(Optional subject)
-
Freie Wahlfächer (
0.0h XX / 6.0 ECTS)
- 312.240 Selected Topics in Statistics (3.0h VU / 5.0 ECTS)
-
Freie Wahlfächer (
0.0h XX / 6.0 ECTS)
-
Subject: Freie Wahlfächer
(Optional subject)
- Master's degree programme Technical Mathematics
(SKZ: 401, Version: 13W.1)
-
Subject: Angewandte Statistik
(Compulsory elective)
-
Ausgewählte Kapitel der Statistik (
3.0h VU / 5.0 ECTS)
- 312.240 Selected Topics in Statistics (3.0h VU / 5.0 ECTS)
-
Ausgewählte Kapitel der Statistik (
3.0h VU / 5.0 ECTS)
-
Subject: Angewandte Statistik
(Compulsory elective)