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.

2022W, 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: 5.10.2022, 16:00 (c.t.), HS 13 Ernst Melan

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

Wed16:00 - 18:0005.10.2022 - 25.01.2023HS 13 Ernst Melan - RPL Lectures
Machine Learning - Single appointments
Wed05.10.202216:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed12.10.202216:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed19.10.202216:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed09.11.202216:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed16.11.202216:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed23.11.202216:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed30.11.202216:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed07.12.202216:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed14.12.202216:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed21.12.202216:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed11.01.202316:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed18.01.202316:00 - 18:00HS 13 Ernst Melan - RPL Lectures
Wed25.01.202316:00 - 18:00HS 13 Ernst Melan - RPL 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, ...)

- Written exam at the end of the semester


DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Tue17:00 - 19:0004.10.2022 https://tuwel.tuwien.ac.at/course/view.php?id=34936written22.09.2022 00:00 - 26.09.2022 23:59TISSExam - 1st retake SS2022: online version (priority for ERASMUS students)
Fri16:00 - 18:0021.10.2022GM 1 Audi. Max.- ARCH-INF written19.09.2022 00:00 - 19.10.2022 23:59TISSExam (1st re-take SS2022)
Tue18:00 - 20:0006.12.2022EI 7 Hörsaal - ETIT assessed14.11.2022 00:00 - 02.12.2022 23:59TISSExam (2nd retake SS 2021)
Fri - 03.02.2023oral08.01.2023 00:00 - 30.01.2023 23:59TISSExam (main date WS2021, 3rd& final retake SS 2021)
Fri - 17.03.2023assessed27.01.2023 12:00 - 10.03.2023 23:59TISSExam (1st retake WS21)
Wed - 17.05.2023assessed20.04.2023 00:00 - 10.05.2023 23:59TISSExam (2nd retake WS 21)

Course registration

Begin End Deregistration end
27.07.2022 00:00 09.10.2022 23:59 13.10.2022 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

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