Modulbeschreibung

AI Applications

ECTS-Credits:
4
Lernziele:

In this module, we focus on advanced AI techniques and their application in software projects. We will discuss and implement different deep learning architectures. 

After successful completion of this module the students are able to:

  1. Understand the fundamentals of Deep Learning. Implement and train a neural network using a Deep Learning framework.
  2. Choose and apply appropriate Deep Learning techniques for a given task. Evaluate the results.
  3. Explain the fundamentals of Reinforcement Learning (RL, deep RL). Apply methods such as SARSA and deep Q-learning.
  4. Explain the basic building blocks of LLMs(e.g. transformers, attention) and learn how RAG architectures work.
  5. Analyze non-technical aspects of AI such as business impact and ethical implications.

Kurse in diesem Modul

AI Applications:

In this module, we cover selected AI topics and apply them to concrete examples. The emphasis will be on commonly used deep learning techniques and their (potential) use in real-world applications. The exact list of topics may vary, but following topics are likely to be covered:

  • Deep learning 
  • CNNs for image classification
  • Reinforcement Learning
  • RNNs or transformers for time-series analysis
  • Sequential Data, Text, Large Language Models (LLMs). Basic RAG architectures
  • AI-model lifecycle management

During the semester the students will implement two graded projects, one of them in a non-technical, interdisciplinary context. 

Vorlesung mit 2 Lektionen pro Woche
Uebung mit 2 Lektionen pro Woche
Disclaimer

Diese Beschreibung ist rechtlich nicht verbindlich! Weitere Informationen finden Sie in der detaillierten Modulbeschreibung.