Main content
CS 542 — Machine Learning
(dt. Maschinelles Lernen)
Level, degree of commitment | Advanced module, compulsory elective module |
Forms of teaching and learning, workload |
Lecture (4 SWS), recitation class (2 SWS), 270 hours (90 h attendance, 180 h private study) |
Credit points, formal requirements |
9 CP Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks. Examination type: Oral examination (individual examination) or written examination |
Language, Grading |
German,The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Data Science. |
Duration, frequency |
One semester, Alle 3-4 Semester |
Person in charge of the module's outline | Prof. Dr. Dominik Heider, Prof. Dr. Matthias Schott |
Contents
Machine learning and data mining methods are central to current research in intelligent systems and are already being used in a variety of practical applications.
Content: introduction and basic concepts, data preprocessing, statistical learning, case-based learning, decision trees, support vector machines, classifier ensembles, empirical evaluation of learning methods.
Qualification Goals
Students will be able to,
- understand basic issues and goals of machine learning,
- deal with special classes of problems, such as supervised learning (classification and regression),
- use important machine learning methods and their scalable implementations,
- apply concepts for the evaluation of learning methods
- solve practical problems independently using machine learning methods,
- to proceed according to scientific working methods (recognizing, formulating, solving problems, abstraction)
- Speak freely about scientific content, both in front of an audience and in a discussion.
Prerequisites
None. The competences taught in the following module are recommended: Introduction to Statistics.
Applicability
Module imported from B.Sc. Data Science.
It can be attended at FB12 in study program(s)
- B.Sc. Data Science
- B.Sc. Computer Science
- M.Sc. Data Science
- M.Sc. Computer Science
- M.Sc. Mathematics
- M.Sc. Business Informatics
- M.Sc. Business Mathematics
- LAaG Computer Science
When studying M.Sc. Mathematics, this module can be attended in the study area Profile Area Computer Science.
Recommended Reading
- D.J. Hand, H. Mannila, P. Smyth. Principles of Data Mining. MIT Press. 2000.
- T. Hastie, R. Tibshirani, J. H. Friedman. The Elements of Statistical Learning. Springer-Verlag, 2001.
- T. Mitchell. Machine Learning. McGraw Hill, 1997.
- I.H. Witten, E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 2000.
- C.M. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, 2008.
Please note:
This page describes a module according to the latest valid module guide in Winter semester 2023/24. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:
- Winter 2016/17
- Summer 2018
- Winter 2018/19
- Winter 2019/20
- Winter 2020/21
- Summer 2021
- Winter 2021/22
- Winter 2022/23
- Winter 2023/24
The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.
The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.