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Ort und Zeit

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

Lehre

Dozent:in: Carola Sophia Heinzel
Assistenz: Samuel Ayomide 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)

Die Zahl der ECTS-Punkte entnehmen Sie bitte den Ergänzungen zu den Modulhandbüchern.