Introduction to Statistical Learning (fu:stat)
"Introduction to Statistical Learning" will give a introduction into the following topics:
- Visualization
- Clustering (SOM and k-means extensions)
- Classification (Decision trees, Boosted trees and Random forest)
- Regression (Ridge regression, Lasso regression and Elastic Net)
Contents
The course provides a first insight into statistical learning, which refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. With the explosion of “Big Data” problems, statistical learning has become a very hot field in many scientific areas as well as marketing, finance and other business disciplines. Besides an introduction to the characteristics, benefits and challenges of Big Data, four applications from the field of statistics are presented. This course addresses all who want to enlarge their knowledge of traditional statistics. Thus, a basic knowledge of statistics (at least up to linear regression) is mandatory. Please note that this course has a statistical focus, therefore technical issues like memory allocations and data storage are not included.
Requirements
Participants should possess knowledge of basic statistical methods, such as hypothesis testing and linear regression.
Lecturers
Felix Skarke
Vincent Keyaniyan
Date
Language
Target Group
Costs
| PhD UP | 25 € |
| Postdoc UP/PNB* | 37.5 € |