Machine Learning
Module IN2064
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.
| 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
| Type | SWS | Title | Lecturer(s) | Dates |
|---|---|---|---|---|
| VI | 6 | Machine Learning (IN2064) | Bojchevski, A. Günnemann, S. Shchur, O. Zügner, D. |
Mon, 10:00–12:00, MW 2001 Tue, 12:15–13:45, MW 0001 Wed, 16:00–19:00, MI HS1 |
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.
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 | |||
| Thu, 2020-02-13, 17:00 till 19:00 | MW: 0001 MW: 2001 00.02.001 MI: 00.02.001 Interims II: 004 Interims I: 101 |
Import | till 2020-01-15 (cancelation of registration till 2020-02-06) |