Machine Learning

Module IN2064

This Module is offered by TUM Department of Informatics.

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

Basic Information

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

Content

kNN & k-means; linear classifiers; linear regression; Bayes: maximum likelihood and maximum a posteriori estimator, mixture of Gaussians, Hidden Markov Models, Expectation Maximization algorithm, nonlinear neural networks and backprop, Support Vector Machines, unsupervised learning

Learning Outcome

Getting familiar with the statistics of machine learning. Basic knowledge of essential learning algorithms. Skill to select, describe and derive appropriate algorithms given a certain problem. Competence to apply these in practical application.

Preconditions

MA0901 Linear Algebra for Informatics, MA0902 Analysis for Informatics, IN0018 Discrete Probability Theory

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

WS 2020/1 WS 2019/20 WS 2018/9 WS 2017/8 WS 2016/7 WS 2015/6

Type	SWS	Title	Lecturer(s)	Dates	Links
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	eLearning documents

Learning and Teaching Methods

Lecture, exercise course, problems for individual study

Media

Slides, videos

Literature

Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, Berlin, New York, 2006.
David J. C. MacKay. Information theory, inference, and learning algorithms. Cambridge Univ. Press, 2008.
Kevin Murphy. Machine Learning: a Probabilistic Perspective. MIT Press. 2012.

Module Exam

Description of exams and course work

Written exam of 120 minutes.

Exam Repetition

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.

Title
Time	Location	Info	Registration
Machine Learning
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)