Please enroll at least a week before course start to get all information. The course is organized as "flipped classroom". This means that you have to watch the course videos before the start of the course. Videos will be uploaded mid February.
Date: 5.3. - 9.3. (block course)
Description
This course will teach the fundamental techniques and concepts of supervised Machine Learning, which has become a central part of modern data analysis. In particular non-linear and non-parametric methods have been used successfully in uncovering complex patterns and relationships by computer scientists and statisticians.
The module offers an introductory and applied overview of supervised learning methods and concepts for regression and classification.This includes models such as linear regression, discriminant analysis, naive bayes, decision trees and random forests, but also more advanced techniques like model selection, feature selection, and hyperparameter optimization. The focus of the course is to give a basic understanding of the different algorithms, models and concepts while explaining the necessary mathematical foundation.
Students acquire theoretical as well as practical competences regarding some fundamental models of learning from data. The students will be enabled to conduct a data analysis project themselves, including understanding and interpreting the data, in order to critically judge advantages and disadvantages of the different methods. The accompanying exercise classes are a mix of theoretical and practical assignments. The latter will be conducted in R and will cover all methods introduced during the lecture.
Vorausgesetzte Vorlesung für den Kurs: Linear Modelle
Einschreibeschlüssel ist: IntroML
- Trainer/in: Bernd Bischl
- Trainer/in: Jann Goschenhofer
- Trainer/in: Christoph Molnar