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 WS 2011/2
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 2022 | SS 2015 | WS 2011/2 |
Basic Information
IN2064 is a semester module in English language at Bachelor’s level and 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. Gao, N. Geisler, S. Gosch, L. Günnemann, S. … (insgesamt 7) |
Mon, 10:00–12:00, GALILEO Audimax Tue, 12:00–14:00, GALILEO Audimax Wed, 16:00–19:00, MI HS1 and singular or moved dates |
eLearning |
Learning and Teaching Methods
Flipped Classroom Lecture on the topics: Probability theory; kNN; multivariate Gaussian; linear regression and classification; kernels; constrained optimization; SVM; GP; neural network; unsupervised learning; expectation maximization; learning theory.
Exercise hours on the above topics
Homework for self-study on the above topics.
Exercise hours on the above topics
Homework for self-study on the above topics.
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
The examination is a written test of 120 minutes. In this exam, students should prove that they are able to select suitable learning algorithms for specific problems and that they understand the probabilistic basics.
Exam Repetition
The exam may be repeated at the end of the semester.