Written on 22.09.23 (last change on 22.09.23) by Osman ali Mian
Registration for the course is now open, you can register until 02.11.2023 23:59.
|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 the basics of programming, proof techniques, linear algebra, and statistics, for example by having taken Programming I and II (for programming), Mathematics for Computer Scientists I and II (for linear algebra), and then either Statistics Lab or Mathematics for Computer Scientists III (for statistics).
|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. Krikamol Muandet|
|Assistants||Osman Ali Mian, Sarah Mameche, Anurag Singh, Nils Walter, Sascha Xu|
|Tutors||Ali Ahmed, Jawad Al Rahwanji, Filippo Garosi, Josephine Joseph, Ahmed Osman, Christina Subedi, and 2 more positions to be filled|
|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 12–14 and Tuesdays 16–18|
|Office Hours||Prof. Dr. Jilles Vreeken and Dr. Krikamol Muandet: after each lecture
Teaching Assistants: by appointment