Lehre
Dozent:in: Carola Heinzel
Assistenz: Samuel Adeosun
Sprache: auf Englisch
Inhalt
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.
Vorkenntnisse
Programmiergrundkenntnisse und Grundkenntnisse in Stochastik.
Verwendbarkeit
Praktische Übung (2HfB21, MEH21, MEB21)
Wahlmodul im Optionsbereich (2HfB21)
Mathematische Ergänzung (MEd18)
Wahlmodul (MSc14)
Elective (MScData24)