Lehrende: Prof. Dr. Ole Wilms
Veranstaltungsart: Vorlesung + Übung
Anzeige im Stundenplan:
Semesterwochenstunden: 3
Credits: 6,0
Unterrichtssprache: Englisch
Min. | Max. Teilnehmerzahl: - | 101
Kommentare/ Inhalte: This bachelor level course will give an introduction to machine learning techniques with a particular focus on how they can be applied in practice. You will learn why, when, and how to apply Big Data methodology to real-world problems. For this, we will review the most common supervised and unsupervised machine learning techniques and learn how they can be implemented in practice. A large focus of the course will be on applications. So you will learn how to work with large datasets using the software package R, apply the appropriate machine learning algorithms and interpret the outcomes. Covered topics:
Lernziel: Upon completion of the course, you should be able to:
Vorgehen: Lectures take place on campus, Tuesday from 16.00-18.30. During these sessions, we will not only cover the course content, but also apply the course material using the software R. Hence, ideally you bring a laptop to class but it is also possible to redo the exercises later at home if you don’t have a laptop. In the beginning of the course, there will be an introductory programming session. In this session you will learn basic programming skills using the software package R as well as how to handle data in R. If you are already familiar with R, you can skip this session. All course materials will be made available on OpenOlat.
Literatur: The course is build around the textbook "An Introduction to Statistical Learning with Applications in R" by James, Witten, Hastie and Tibshirani. The book as well as datasets and practice exercises are available at https://statlearning.com/
Zusätzliche Hinweise zu Prüfungen: The language of examination is the same as the language of instruction.