After successful completion of the course, students are able to

• explain and formulate the theoretical concepts of important dimension reduction techniques and methods for linear and nonlinear regression and classification
• identify the strengths and weaknesses of the different statistical methods and tools and to use them in practice

During the past decade there has been an explosion in computation and information technology. With it has come vast amount of data in a variety of fields such as medicine, finance and marketing. The challange to understand these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning and bioinformatics. This lecture describes the important ideas in these areas in a common conceptual framework. The many topics include neural networks, support vector machines, classification trees and generalized additive models.

Examples with data, software environment R

The practice part takes place online. The other part will be held in the following rooms: Wednesday, 5 October 2022 - 11 January 2023, 8:00 - 10:00, HS 17 Friedrich Hartmann and from 18 January 2023 in GM 5 Praktikumshörsaal.

Detailed information under VU 105.707.

Solving examples in R, oral exam

Lecture notes for this course are available from the lecturer.