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.
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
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VI | 6 | Machine Learning (IN2064) | Bilos, M. Charpentier, B. Geisler, S. Günnemann, S. Schuchardt, J. … (insgesamt 7) |
Wed, 16:00–19:00 Mon, 10:00–12:00 Tue, 12:15–13:45 |
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.
David J. C. MacKay. Information theory, inference, and learning algorithms. Cambridge Univ. Press, 2008.
Kevin Murphy. Machine Learning: a Probabilistic Perspective. MIT Press. 2012.