Modulbeschreibung

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.

This module is taught in English. Most of the AI Literature is in English. An intermediate level is recommended. The students are free to write reports in German or English. The exam questions will be in English, the students are free to write answers in German or English.

The lecture will be streamed and recorded.

Zwei Projekte mit Kurzbericht

Schriftliche Prüfung 50%

Projekte 50%

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.