This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2015 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|SS 2015||WS 2011/2|
IN2064 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|240 h||90 h||8 CP|
Content, Learning Outcome and Preconditions
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VI||6||Machine Learning (IN2064)||Bojchevski, A. Günnemann, S. Klicpera, J. Lienen, M. Shchur, O.||
Tue, 12:15–13:45, virtuell
Wed, 16:00–19:00, virtuell
Mon, 10:00–12:00, virtuell
Learning and Teaching Methods
David J. C. MacKay. Information theory, inference, and learning algorithms. Cambridge Univ. Press, 2008.
Kevin Murphy. Machine Learning: a Probabilistic Perspective. MIT Press. 2012.
Description of exams and course work
The exam may be repeated at the end of the semester.
Current exam dates
Currently TUMonline lists the following exam dates. In addition to the general information above please refer to the current information given during the course.
|Tue, 2021-02-16, 11:00 till 13:00||graded online exercise https://www.tum.de/die-tum/aktuelles/coronavirus/corona-lehre-pruefungen/||till 2021-01-15 (cancelation of registration till 2021-02-09)|