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Information on the practical exercise Machine Learning (SS 2025)

Lecturer: Carola Heinzel

Assistant: Samuel Adeosun

Date: Do, 14-16 Uhr, PC-Pool Raum -100, Hermann-Herder-Str. 10

Language: english

 

Current

 Please register for the Ilias course. The password will be given to you in the first lecture.

 

Please download Jupyter Notebook. Please contact Samuel Adeosun , if you have any problems to download it. You can bring your laptop to the lectures or use the computers in the PC-pool.

 

A very easy way to install Jupyter Notebook is to install Anaconda/Miniconda here.

Content

This course introduces the foundational concepts and practical skills necessary for understanding and implementing machine learning models, with a particular focus on deep learning and neural networks. Students will progress from basic programming skills in Python , with a focus on the PyTorch library, to advanced topics such as training multi-layer perceptrons, optimization techniques, and transformer architectures. By the end of the course, participants will have the ability to implement and analyze neural networks, apply optimization strategies, and understand modern transformer-based models for tasks such as text generation and time series analysis.

 

Previous knowledge

Basic knowledge of programming and basic knowledge of stochastics.

 

Literature

Parts of the lecture are based on this  GitHub page.

Studienleistung

You have to earn at least 50% of the maximally accessible exercise points and  present one solution of an exercise in the tutorial. Moreover, a short presentation of a small Python project at the end of the semester is mandatory.

 

Usability

Praktische Übung (2HfB21, MEH21, MEB21)
Wahlmodul im Optionsbereich (2HfB21)
Mathematische Ergänzung (MEd18)
Wahlmodul (MSc14)
Wahlmodul (MScData24)

Consultation hour

Lecturer consultation hours: Monday from 10 to 11 AM or by appointment upon request via email
Consultation hour assistant: By arrangement