Elements of Statistical Learning
General Information
- This course will be offered online starting April 21st. It is strongly intended to follow the original semester schedule, thus to finish the course by July 24th.
- For further information on how the lecture and exercises will be carried out, see below.
- This is a completely new situation and it involves definitely experimenting and trial and error! So please interact with us and let us know what works and what does not and tell us how you get along with the format of the course!
Content
- After successful completion of the course students are able to understand and to apply basic concepts and methods of supervised and unsupervised statistical learning on large data (using R). They will have learnt fundamental concepts in statistical learning, with a focus on probabilistic formulation of the various learning problems and they will have an overview over different methods and their applications. Furthermore, they can adapt learning algorithms to new models and analyze new data with them.
- A selection of topics;
- statistical learning, supervised/unsupervised
- assessing model accuracy, bias-variance trade-off
- In the field of supervised learning we will study
- high-dimensional regression & shrinkage methods
- linear classification, logistic regression
- resampling: cross validation, bootstrap
- nonlinear classification, decision trees and random forests
- boosting
- In the field of unsupervised learning we will study
- dimension reduction, principal component analysis
- clustering, mixture models
- data analysis in R
Literature
- James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer Texts in Statistics. Springer New York.
- Berk, R. (2008). Statistical Learning from a Regression Perspective. Springer Series in Statistics. Springer.
Time
The institute is offering all courses originally planned starting April 20th. Teaching will take place online using the university's moodle system. Further information is available on the moodle system.
Type
- Mathematics, B.Sc., Compulsory electives in Applied Mathematics
- Mathematical Biometry, B.Sc., Compulsory electives in Stochastics
- Mathematics and Management, B.Sc., Compulsory electives in Stochastics, Optimisation, Financial Mathematics
- Computational Science and Engeneering, M.Sc., Compulsory electives
Prerequisites
- Foundations of Analysis and Lineare Algebra, solid basis in probabilty theory and statistics (as provided by the modules Elementary Probability theory and Statistics or Applied Stochastics 1 + 2)