184.702 Machine Learning
Diese Lehrveranstaltung ist in allen zugeordneten Curricula Teil der STEOP.
Diese Lehrveranstaltung ist in mindestens einem zugeordneten Curriculum Teil der STEOP.

2019W, VU, 3.0h, 4.5EC
Diese Lehrveranstaltung wird nach dem neuen Modus evaluiert. Mehr erfahren



  • Semesterwochenstunden: 3.0
  • ECTS: 4.5
  • Typ: VU Vorlesung mit Übung


Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage..

- Formulate problems as specific Machine Learning tasks

- Understand of a range of machine learning algorithms and their characteristics

- Select the fitting methods for a specific learning goal

- Explain data preprocessing techniques

- Evaluate the methods for their suitability




Inhalt der Lehrveranstaltung

Principles of Supervised and Unsupervised Machine Learning, including pre-processing and Data Preparation, as well as Evaluation of Learning Systems. Machine Learning models discussed may include e.g. Decision Tree Learning, Model Selection, Bayesian Networks, Regression techniques, Support Vector Machines, Random Forests as well as ensemble methods.

Preliminary talk: 2.10. 2019


The course contains classroom lectures and exercises. Exercises include the application of machine learning techniques for various data sets and implementation of machine learning algorithms. The exercises are prepared at home and will be presented/discussed during the exercise classes. 



Weitere Informationen

This course will be held completely in TUWEL - all lecture materials and news about the lecture will be made available there, and all questions regarding the course should be asked in the TUWEL forum *only*, not via TISS.

To get access to the TUWEL course, just apply to the group in TISS, and then follow the TUWEL link above


ECTS Breakdown:

8 classes (including prepration): 22 h

4 classes for presentations/discussions (including preparation): 12

Assignments: 46.5 h

exam: 32 h


total: 112.5 h



LVA Termine

Mi.16:00 - 18:0002.10.2019 - 29.01.2020EI 8 Pötzl HS Vorlesung
Mo.13:00 - 16:0016.12.2019Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 2
Di.10:00 - 12:3017.12.2019FAV Hörsaal 2 Presentations Exercise 2
Di.14:00 - 18:0017.12.2019Seminarraum FAV 01 C (Seminarraum 188/2) Presentations Exercise 2
Mi.12:00 - 14:0018.12.2019FAV Hörsaal 2 Presentations Exercise 2
Do.11:00 - 16:0019.12.2019Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 2
Do.12:00 - 17:0019.12.2019EI 8 Pötzl HS Presentations Exercise 2
Mi.11:00 - 13:0022.01.2020Seminarraum FAV EG C (Seminarraum Gödel) Presentations Exercise 3
Do.12:00 - 17:0030.01.2020FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations Exercise 3
Machine Learning - Einzeltermine
Mi.02.10.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.09.10.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.16.10.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.23.10.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.30.10.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.06.11.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.13.11.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.20.11.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.27.11.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.04.12.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.11.12.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Mo.16.12.201913:00 - 16:00Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 2
Di.17.12.201910:00 - 12:30FAV Hörsaal 2 Presentations Exercise 2
Di.17.12.201914:00 - 18:00Seminarraum FAV 01 C (Seminarraum 188/2) Presentations Exercise 2
Mi.18.12.201912:00 - 14:00FAV Hörsaal 2 Presentations Exercise 2
Mi.18.12.201916:00 - 18:00EI 8 Pötzl HS Vorlesung
Do.19.12.201911:00 - 16:00Seminarraum FAV 01 B (Seminarraum 187/2) Presentations Exercise 2
Do.19.12.201912:00 - 17:00EI 8 Pötzl HS Presentations Exercise 2
Mi.08.01.202016:00 - 18:00EI 8 Pötzl HS Vorlesung
Mi.15.01.202016:00 - 18:00EI 8 Pötzl HS Vorlesung


- Solving of exercises regarding experiments in machine learning, using a software toolkit of the student's choice (e.g. Python scikit-learn, Matlab, R, WEKA, ...)

- Written exam at the end of the semester


Mi.15:00 - 17:0029.01.2020EI 7 Hörsaal schriftlich01.01.2020 00:00 - 26.01.2020 23:59in TISSExam (main date winter semester, last retake summer semester)
Fr.14:00 - 16:0020.03.2020Informatikhörsaal beurteilt31.01.2020 12:00 - 18.03.2020 00:00in TISSExam - 1st Retake
Mi.17:00 - 19:0020.05.2020Informatikhörsaal beurteilt23.04.2020 00:00 - 18.05.2020 23:59in TISSExam - 2nd Retake
Do.11:00 - 13:0025.06.2020EI 7 Hörsaal schriftlich20.05.2020 00:00 - 23.06.2020 23:59in TISSExam (main date summer semester, last retake winter semester)


Von Bis Abmeldung bis
31.07.2019 00:00 16.10.2019 23:59 16.10.2019 23:59


Acceptance to the course will be by the lecturers. Priority is given to

1.) Students that have this course as a compulsory or elective course (i.e. most computer science studies)

2.) ERASMUS students that have Machine Learning in their learning agreement.

3.) PhD students from the Faculty of Informatics

4.) Students that are currently in a bachelor programme of any of the studies mentioned in 1.), and are finishing their studies in the current semester. You will need to contact the lectureres and state your expected graduation, and which master programme you will continue

5.) If there are still free places afterwards, they will be assigned to master and PhD students from other faculties, and finally to all other students from other faculties. You need to contact the lecturers and state why the course is important for your studies.



Es wird kein Skriptum zur Lehrveranstaltung angeboten.


Self-Organising Systems (188.413) offers complementary topics in unsupervised data analysis. Information Retrieval (188.412) applies principles from Data Mining, Machine Learning

Problem Solving and Search in Artificial Intelligence (181.190) teaches some problem solving techniques that can be used in machine learning 


Begleitende Lehrveranstaltungen

Vertiefende Lehrveranstaltungen