Laboratory for applied machine learning algorithms

Lecture languageGerman


Please refer to the respective ILIAS course for the specific dates.

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Laboratory for Applied Machine Learning Algorithms (LAMA)


Basic programming skills are required. Knowledge of the fundamentals of information technology, signal and system theory and probability theory is also required.


The topics of machine learning and artificial intelligence are playing an increasingly dominant role in our society. This is due to the increasing computing power and the availability of new technologies (modern GPUs, multi-cores, FPGAs, etc.) in combination with efficient parallelized frameworks and algorithms for training neural networks and generative AIs.

As data volumes are collected and processed in all areas of life, the areas of application and research in the field of machine learning are far-reaching. For example, convolutional neural networks (CNNs) are already replacing traditional object recognition methods in image processing for autonomous driving. In medical technology, work is being carried out on artificial neural networks that can already recognize malignant skin changes more reliably than doctors on the basis of images. Through models such as the Generative Pre-trained Transformer (GPT), AI applications such as ChatGPT, Bard and Llama have become part of everyday life for many people, including students. Machine learning is therefore becoming an increasingly important part of information technology for prospective electrical engineers.

In order to meet these future challenges as an engineer, it is essential to build up the necessary skills during your studies and to gain a basic understanding of the methods and tools in the field of machine learning.

The course teaches the practical use of common machine learning algorithms and methods in a project-based and hands-on approach. Working in groups and using powerful workstations, you will independently implement algorithms and structures such as perceptrons, decision trees and evolutionary algorithms. At the same time, you will also learn how to use methods and tools that are widely used in business and science (Keras, Tensorflow, Pytorch, Cuda,...) to solve real-world problems (rental prices, medical data,...).

In the "Into-the-Wild" part, you have the exciting opportunity to choose from applied problems from research or your own ideas! The spectrum is wide and ranges from detections in particle physics to the prediction of soccer matches - everything has been included!

Organizational matters

IntroPreliminary discussion and group allocationWednesday25.10.2023ITIV room 216, 2-4 p.m.
Task 1Processing and analysis of data setsWednesday08.11.2023ITIV room 216, 2-6 p.m.
Task 2Evaluation of ML systemsWednesday15.11.2023ITIV room 216, 2-6 p.m.
Task 3Basics of supervised learningWednesday22.11.2023ITIV room 216, 2-6 p.m.
Task 4Unsupervised learningWednesday29.11.2023ITIV room 216, 2-6 p.m.
Task 5Evolutionary AlgorithmsWednesday06.12.2023ITIV room 216, 2-6 p.m.
Task 6Neural NetworksWednesday13.12.2023ITIV room 216, 2-6 p.m.
Task 7Convolutional Neural NetworksWednesday20.12.2023ITIV room 216, 2-6 p.m.
Task 8Recurrent Neural NetworksWednesday10.01.2024ITIV room 216, 2-6 p.m.
ItW 1Into the Wild...Wednesday17.01.2024ITIV room 216, 2-6 p.m.
ItW 2Into the Wild...Wednesday24.01.2024ITIV room 216, 2-6 p.m.
ItW 3Into the Wild...Wednesday31.01.2024ITIV room 216, 2-6 p.m.
ItW 4Into the Wild...Wednesday07.02.2024ITIV room 216, 2-6 p.m.
LectureLectureWednesday/Thursday14.&15.02.2024ITIV room 216, 2-6 p.m.
19.02.2024 until 22.02.2024

Attendance is compulsory for all dates and the preliminary meeting.

  • You will work on the tasks in teams of two or three.
  • The assessment is made up of the submitted task sheets, the "Into the wild" part and a colloquium.
  • Further details on the assessment will be explained in the introductory event.
  • Six ETCS points are awarded.

Course content

  • Preliminary discussion and group allocation
  • Processing and analysis of data sets
  • Evaluation of ML systems
  • Basics of supervised learning
  • Unsupervised learning
  • Neural networks
  • Evolutionary algorithms
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Into the Wild...

Procedure of the lab sessions 1-8

  • During the individual lab sessions, students work on predefined tasks. These include programming tasks as well as tasks to be answered in text form.
  • The task sheets are issued in the form of Jupyter notebooks. With this interactive development environment, you can test program codes directly in the task sheet, display solutions and complete the documentation or answers.
  • After each laboratory session, the completed task sheet (the corresponding Jupyter notebook) is handed in.
  • It is sufficient to hand in one copy of the notebook per group.
  • Successful completion and submission of the task sheets is a prerequisite for participation in the oral colloquium at the end of the semester. Participants who fail to hand in their work will not be admitted to the colloquium!

Into the Wild...

  • At the beginning of the second part, various data sets are presented, which are available for processing.
  • Each group chooses one of the presented data sets, of course several groups can use the same data set. The problem is defined by the group itself. It is also possible to create your own problems for your own data sets.
  • A concept for solving the problem is developed on the basis of what has been learned previously. This is implemented and tested.
  • In addition to working on the data, it is also possible to optimize an existing approach for a data set in terms of runtime or latency. Among other things, an attempt can be made to achieve this using suitable hardware.
  • Finally, each group prepares a presentation that introduces the developed concept and presents the results. Possible next steps should also be identified by critically reflecting on the decisions made previously.

Application procedure

This year, 30 places are expected to be offered for the lab.
You can register for the lab via the WiWi portal at the following link:


The necessary materials for the LAMA will be made available via ILIAS. After successfully registering for LAMA, you will receive access to the tasks, data sets and additional information material.
The material is provided in English, but can be edited in German.

Tutors wanted

For former participants or other Master's students who would like to work on the topics, there are opportunities to be a tutor during the course. If you are interested, please send an e-mail with the appropriate subject to the contact address: lama∂