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184.702 Machine Learning
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020S, VU, 3.0h, 4.5EC


  • Semester hours: 3.0
  • Credits: 4.5
  • Type: VU Lecture and Exercise

Learning outcomes

After successful completion of the course, students are able to...

- 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

Subject of course

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.

Didactical Concept:
students will compare different machine algorithms for particular data sets, and have to implement a machine learning algorithm - Presentation of algorithms by students - Discussion of reports that summarize the comparison of machine learning algorithms

Assessment: is based on written exam, report, and implemented machine learning algorithms

Preliminary talk (Vorbesprechung) & intro: 3.3. 2020

Teaching methods

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. 

Mode of examination


Additional information

This course will be held in both summer and winter term from, summer semester 2019 on.


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:

13 classes (including prepration): 34 h

3 classes for presentations/discussions (including preparation): 9

Assignments: 39.5 h

exam: 30 h


total: 112.5 h





Course dates

Tue12:00 - 14:0003.03.2020 - 10.03.2020EI 8 Pötzl HS - QUER Lectures
Machine Learning - Single appointments
Tue03.03.202012:00 - 14:00EI 8 Pötzl HS - QUER Lectures
Tue10.03.202012:00 - 14:00EI 8 Pötzl HS - QUER Lectures

Examination modalities

- 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, ...): 50%

- Written exam at the end of the semester: 50%


DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Fri16:00 - 18:0018.10.2024GM 1 Audi. Max.- ARCH-INF written16.09.2024 00:00 - 16.10.2024 23:59TISSExam (2024S 1st re-take)
Tue18:00 - 20:0003.12.2024EI 7 Hörsaal - ETIT assessed29.10.2024 00:00 - 29.11.2024 23:59TISSExam (2024S 2nd & final retake)
Tue12:00 - 14:0021.01.2025GM 1 Audi. Max.- ARCH-INF assessed29.12.2024 00:00 - 16.01.2025 23:59TISSExam (2024W main date)
Thu13:00 - 15:0006.03.2025EI 7 Hörsaal - ETIT assessed05.02.2025 00:00 - 03.03.2025 23:59TISSExam (2024W 1st re-take)
Tue17:00 - 19:0029.04.2025Informatikhörsaal - ARCH-INF assessed28.03.2025 23:00 - 24.04.2025 23:59TISSExam (2024W 2nd & final re-take)
Wed15:00 - 17:0025.06.2025GM 1 Audi. Max.- ARCH-INF written26.05.2025 00:00 - 22.06.2025 23:59TISSExam (2025S main date)

Course registration

Begin End Deregistration end
11.12.2019 12:00 18.03.2020 23:59 18.03.2020 23:59



No lecture notes are available.

Previous knowledge

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

As a subsequent course, Problem Solving and Search in Artificial Intelligence (181.190) teaches some problem solving techniques that can be used in machine learning.

Accompanying courses

Continuative courses