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.

2023W, VU, 3.0h, 4.5EC


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

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, Deep Learning, Random Forests as well as ensemble methods.

Preliminary talk: 03.10.2023, 17:00, EI9

Further schedule: see TUWEL

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 are presented/discussed during the exercise classes. 

Mode of examination


Additional information

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): 26 h

2 classes for presentations/discussions (including preparation): 8

Assignments: 46.5 h

exam: 32 h


total: 112.5 h




Course dates

Tue17:00 - 19:0003.10.2023 - 23.01.2024EI 9 Hlawka HS - ETIT Lecture
Thu16:00 - 18:0012.10.2023 - 18.01.2024EI 9 Hlawka HS - ETIT Lecture
Thu18:00 - 19:0021.12.2023EI 9 Hlawka HS - ETIT Lecture
Machine Learning - Single appointments
Tue03.10.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Tue10.10.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Thu12.10.202316:00 - 18:00EI 9 Hlawka HS - ETIT Lecture
Tue17.10.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Thu19.10.202316:00 - 18:00EI 9 Hlawka HS - ETIT Lecture
Tue24.10.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Tue31.10.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Tue07.11.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Thu09.11.202316:00 - 18:00EI 9 Hlawka HS - ETIT Lecture
Tue14.11.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Tue21.11.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Thu23.11.202316:00 - 18:00EI 9 Hlawka HS - ETIT Lecture
Tue28.11.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Thu30.11.202316:00 - 18:00EI 9 Hlawka HS - ETIT Lecture
Tue05.12.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Thu07.12.202316:00 - 18:00EI 9 Hlawka HS - ETIT Lecture
Tue12.12.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Thu14.12.202316:00 - 18:00EI 9 Hlawka HS - ETIT Lecture
Tue19.12.202317:00 - 19:00EI 9 Hlawka HS - ETIT Lecture
Thu21.12.202316:00 - 18:00EI 9 Hlawka HS - ETIT Lecture

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, ...)

- Written exam at the end of the semester


DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Thu13:00 - 15:0007.03.2024EI 7 Hörsaal - ETIT assessed07.02.2024 00:00 - 04.03.2024 23:59TISSExam (WS2023 1st re-take)
Tue15:00 - 17:0030.04.2024Informatikhörsaal - ARCH-INF assessed29.03.2024 23:00 - 25.04.2024 23:59TISSExam (WS2023 2nd & final re-take)
Wed - 26.06.2024written27.05.2024 00:00 - 23.06.2024 23:59TISSExam (2024 main date)

Course registration

Begin End Deregistration end
26.07.2023 00:00 03.10.2023 18:00 04.10.2023 18:00

Registration modalities

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 (Rudolf Mayer, Nysret Musliu) 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 (Rudolf Mayer, Nysret Musliu) and state why the course is important for your studies. Note that the registration can be confirmed only when for the registration period ends.



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

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