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.

2023S, VU, 3.0h, 4.5EC


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

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, Naive Bayes, Bayesian Networks, Basic Regression Techniques, Support Vector Machines, Random Forests, Perceptron, Neural Networks, Deep Learning, as well as ensemble methods. The course also gives a short introduction to Automated Machine Learning and Reinforcement Learning. 

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:  01.03.2023, in-class

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 support will be only 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

Wed12:00 - 14:0001.03.2023 - 28.06.2023EI 10 Fritz Paschke HS - UIW Lecture
Tue12:00 - 14:0007.03.2023 - 20.06.2023EI 7 Hörsaal - ETIT Lecture
Tue12:00 - 14:0002.05.2023HS 7 Schütte-Lihotzky - ARCH Lecture
Wed12:00 - 14:0010.05.2023EI 3 Sahulka HS - UIW Lecture
Machine Learning - Single appointments
Wed01.03.202312:00 - 14:00EI 10 Fritz Paschke HS - UIW Lecture
Tue07.03.202312:00 - 14:00EI 7 Hörsaal - ETIT Lecture
Wed08.03.202312:00 - 14:00EI 10 Fritz Paschke HS - UIW Lecture
Tue14.03.202312:00 - 14:00EI 7 Hörsaal - ETIT Lecture
Wed15.03.202312:00 - 14:00EI 10 Fritz Paschke HS - UIW Lecture
Tue21.03.202312:00 - 14:00EI 7 Hörsaal - ETIT Lecture
Wed22.03.202312:00 - 14:00EI 10 Fritz Paschke HS - UIW Lecture
Tue28.03.202312:00 - 14:00EI 7 Hörsaal - ETIT Lecture
Wed29.03.202312:00 - 14:00EI 10 Fritz Paschke HS - UIW Lecture
Tue18.04.202312:00 - 14:00EI 7 Hörsaal - ETIT Lecture
Wed19.04.202312:00 - 14:00EI 10 Fritz Paschke HS - UIW Lecture
Tue25.04.202312:00 - 14:00EI 7 Hörsaal - ETIT Lecture
Wed26.04.202312:00 - 14:00EI 10 Fritz Paschke HS - UIW Lecture
Tue02.05.202312:00 - 14:00HS 7 Schütte-Lihotzky - ARCH Lecture
Wed03.05.202312:00 - 14:00EI 10 Fritz Paschke HS - UIW Lecture
Tue09.05.202312:00 - 14:00EI 7 Hörsaal - ETIT Lecture
Wed10.05.202312:00 - 14:00EI 3 Sahulka HS - UIW Lecture
Tue16.05.202312:00 - 14:00EI 7 Hörsaal - ETIT Lecture
Wed17.05.202312:00 - 14:00EI 10 Fritz Paschke HS - UIW Lecture
Tue23.05.202312:00 - 14:00EI 7 Hörsaal - 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, ...): 50%

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

- Written exam - most likely on-line via TUWEL. If the pandemic situation allows face-to-face exams at the scheduled time, we would switch to an in-class exam.


DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Wed15:00 - 17:0026.06.2024GM 1 Audi. Max.- ARCH-INF written27.05.2024 00:00 - 23.06.2024 23:59TISSExam (SS2024 main date)

Course registration

Begin End Deregistration end
14.12.2022 12:00 07.03.2023 23:59 07.03.2023 23:59

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

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, and "Security, Privacy and Explainability in Maschine Learning" (194.055) offers topics in privacy-preserving machine learning (e.g. federated learning) and security of machine learning (e.g. adversarial attacks, model stealing, ...)

Accompanying courses

Continuative courses