The lecture assumes basic knowledge in statistics, linear algebra, and programming. It is advisable to have successfully completed Mathematics for Computer Scientists 2 and Statistics Lab. The exercises use the programming language R. We will give a basic introduction to R in the first tutorial. In addition, for preparation the following materials are useful: R for Beginners by Emmanuel Paradis (especially chapters 1, 2, 3 and 6) and An introduction to R (Venables/Smith).
Prerequisite for admission to the examination is a cumulative 50% of the points of the theoretical and a cumulative 50% of the points of the practical tasks on the exercise sheets. Depending on the number of participants, the examinations are either written or oral. The final modality will be announced in the first two weeks of the lecture.
Will be determined from performance in exams.
2 h lectures + 2 h tutorial = 4 h (weekly)
60 h of classes + 120 h private study = 180 h (= 6 ECTS)
In this course we will discuss the foundations – the elements – of machine learning. In particular, we will focus on the ability of, given a data set, to choose an appropriate method for analyzing it, to select the appropriate parameters for the model generated by that method and to assess the quality of the resulting model. Both theoretical and practical aspects will be covered. What we cover will be relevant for computer scientists in general as well as for other scientists involved in data analysis and modeling.
The lecture covers basic machine learning methods, in particular the following contents:
The course broadly follows the book An Introduction to Statistical Learning with Applications in R, Springer (2013). In some cases, the course receives additional material from the book The Elements of Statistical Learning, Springer (second edition, 2009). The first book is the introductory text, the second covers more advanced topics. Both books are available as free PDFs. Any change of, or additional material will be announced before the start of the course on the course webpage.
This module is part of the following study programmes: