Doctoral candidates and early-stage researchers face a wide variety of professions and career paths open to them. Launching a career and attaining long-term success in these professional fields often demands a wide range of competencies. The Graduate Centre offers training programmes that specifically cater to the needs of early career researchers.
While our information events address cross-disciplinary aspects relevant to early-stage researchers' qualification, our workshops (organized by the Centre for Careers and Competencies) aim at supporting and expanding the skills involved in conducting research.
Termine am Samstag. 04.12.21 - Sonntag. 05.12.21 09:00 - 13:00, Ort: (ONLINE über Zoom)
Termine am Freitag. 19.11.21 09:00 - 13:30, Ort: (ONLINE (zusätzlich Vor- und Nachbereitung als blended Learning))
The foundation of any scientific work at the University of Passau is compliance with the recognised principles of science such as being honest, working lege artis as well as documenting and constantly questioning all results. Scientific misconduct harms the person concerned, the university and science in general. In order to increase awareness of the basic rules and to strengthen the researchers' trust in one another, the Graduate Centre offers an e-learning course free of charge entitled "Good academic practice".
Please contact us if you would like to participate in the e-learning course. You will receive log-in data and further information that will allow you to take part in the e-learning course anytime from your computer.
For further information events, workshops and other events offered by the Graduate Centre in German please refer to the German version of this website.
The faculties pool our researchers' expertise. Although the university does not offer a comprehensive doctoral curriculum, the faculties offer a variety of lectures and seminars that help doctoral researchers deepen their disciplinary knowledge. Each semester, these events are carefully selected to meet doctoral researchers' needs.
35622 Vorlesung: Computational Statistics - Statistical Learning in R (WiSe 21/22)
ZeitenDi. 12:00 - 14:00 (wöchentlich) - Online-Veranstaltung
Erster TerminDi., 19.10.2021 12:00 - 14:00 Uhr, Ort: (Online)
BeschreibungStatistical Learning sums up methods from computational statistics that are designed to deal with high dimensional, complex data sets. Various topics that facilitate modeling of and gaining a deeper insight into high dimensional, complex data sets are introduced. Basic subervised and unsupervised statistical learning techniques are presented, discussed, and applied in class (For example hierarchical clustering, linear and nonlinear classification and regression techniques, incorporating lasso, random forests, bagging, boosting, etc.). Meta-parameter selection, model evaluation, and specification choice in practical settings are also covered in the course.
HeimateinrichtungLehreinheit für Computergestützte Statistik und Mathematik
VoraussetzungenKnowledge of statistics and regression methods on master level and basic knowledge of R (e.g. via 'Computational Statistics – Regression in R').
LernorganisationGuided computer tutorials; students are expected to deepen their knowledge by completing self-contained exercises in R.
LeistungsnachweisFinal exam (60 minutes) or performance assessment at home; R-skills are certified via a certificate when the final exam is passed.
- Kuhn, M. & Johnson, K. (2013), Applied Predictive Modeling, Springer.
- Hastie, T., Tibshirani, R. & Friedman, J. (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2Ed., Springer.
- Efron, B., Hastie, T. (2016), Computer Age Statistical Inference, Cambridge University Press.
- Torgo, L. (2017), Data Mining with R: Learning with Case Studies, 2Ed., CRC Press.
- James, G., Witten, D., Hastie, T & Tibshirani, R. (2015), An Introduction to Statistical Learning: with Applications in R, Springer.
Hinweise zur Anrechenbarkeit
SonstigesCourse is taught in english.
For further lectures and seminars offered by the faculties in German please refer to the German version of this website.