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Course Information

Summary 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.

The course is targeted at students in computer science, data science and AI, cybersecurity, bioinformatics, math, and general sciences with a mathematical background. Students should know linear algebra and have good basic knowledge of statistics, for example by having taken Mathematics for Computer Scientists I and II (for linear algebra), and then either Statistics Lab or Mathematics for Computer Scientists III.

We provide a self-test that you can use to evaluate whether you have the required background to attempt EML.

Type Basic Lecture (6 ECTS) for BSc DSAI, CySec, and Computer Science; Advanced Lecture (6 ECTS) for all others.
Lecturers Prof. Dr. Jilles Vreeken and Dr. Aleksandar Bojchevski
Assistants Advait Gadhikar, Janis Kalofolias, David Kaltenpoth, Sarah Mameche
Tutors Lukas Auer, Ihor Hetman, Kinaan Khan, Maryam Meghdadi, Michael Schott, Sohaib Zahid
Lectures Thursdays, 16–18 o'clock in person in E.2.2 Lecture Hall 0.01 (Günter Hotz Hörsaal) and online via Zoom and YouTube
Assignments 6 assignment sheets, one every two weeks, including theory, practical and bonus questions.
Tutorials Mondays and Tuesdays, 12–14 o'clock in-person and via Zoom
Office Hours Prof. Dr. Jilles Vreeken and Dr. Aleksandar Bojchevski: after each lecture
Teaching Assistants: by appointment
Language English
Contact Via the Forum, for exceptional cases you can write to eml-tas (at) to When in doubt, check the Questions and Answers page. 
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