Introduction to neural networks and deep earning

Network architectures

Neural network training

Alternative training methods

Further network architectures

Use case evaluation

Model-centric evaluation

Business-centric evaluation

Monitoring

Avoiding common fallacies

Change management

Introduction to reinforcement learning

Markov chains

Bandit

Q-Learning

Reinforcement learning approaches

In this module, students will reflect on current developments in AI. To this end, pertinent topics will be introduced via articles that are then critically evaluated by the students in the form of a written essay.

In the course “Project: AI Use Case”, students choose a project task in accord with their tutor from a variety of options. The goal is to prototypically implement an artificial intelligence model or system in a suitable development environment. The choice of approach, the system or software implemented, and the resulting performance on the task is to be reasoned about, explained, and documented in a project report. To this end, students make practical use of the methodological knowledge acquired in the previous courses by applying them to relevant real-world problems.

Computer Vision And NLP

Advanced Robotics 4.0

Applied Autonomous Driving