389.204 Machine Learning Algorithms
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, 4.0h, 6.0EC


  • Semester hours: 4.0
  • Credits: 6.0
  • Type: VU Lecture and Exercise
  • Format: Hybrid

Learning outcomes

After successful completion of the course, students are able to understand basic methods in the field of machine learning and to solve typical problems by applying sophisticated machine learning algorithms.

Subject of course

The course provides a practical and theoretical overview of machine learning methods: General concepts of machine learning (supervised vs. unsupervised approaches, generalization and overfitting, regularization techniques, evaluation methods, cross-validation, early stopping). Basic regression and classification algorithms (least squares, perceptron and logistic regression). Basics of clustering (Lloyd, kMeans). Nonlinear extensions through the kernel trick (support vector machines, kernel ridge). Gradient descent and neural networks (backprogation, LMS, RLS, Newton). Domain specific layers (Recursive and convolutional networks) and unsupervised neural networks (autoencoders).

Teaching methods

The lectures are complemented by 4 exercise classes with a focus on practical implementations in Tensorflow and Numpy. Basic Python knowledge is beneficial but not required. At the end of the course the students will work on a practical project, undergoing a complete machine learning pipeline on a real-world dataset. Here we also cover more advanced topics (adversarial networks, transfer learning).

Mode of examination


Additional information

The first lecture will be held in presence in EI1 on October 6, 2023, 2pm-3:30pm.

This years exercise classes will be held on:







Course dates

Fri14:00 - 16:0006.10.2023 - 19.01.2024EI 1 Petritsch HS Vorlesung
Machine Learning Algorithms - Single appointments
Fri06.10.202314:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri13.10.202314:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri20.10.202314:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri27.10.202314:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri03.11.202314:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri10.11.202314:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri17.11.202314:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri24.11.202314:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri01.12.202314:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri15.12.202314:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri12.01.202414:00 - 16:00EI 1 Petritsch HS Vorlesung
Fri19.01.202414:00 - 16:00EI 1 Petritsch HS Vorlesung

Examination modalities

Students gain points in the 4 exercises classes by submitting homeworks and presenting their solutions. The obligatory project report handed in at the end of the semester acts as the basis for the final oral exam.

Course registration

Not necessary


Study CodeObligationSemesterPrecon.Info
066 507 Telecommunications Mandatory elective


No lecture notes are available.

Previous knowledge

SP1 and 2 should be an active knowledge.