Main topics: 

 

Description:

The field of deep learning for time series data is growing rapidly, enabling breakthroughs in areas such as healthcare, manufacturing, and logistics. This seminar leverages expertise in time series analysis, explainable AI (XAI) for time series, and multimodal learning, to equip students with cutting-edge techniques for handling and interpreting time series data in real-world contexts. 

The seminar will explore advances in deep learning with practical, hands-on work using the Tool Tracking dataset, which employs smart sensors in tools to improve efficiency and accuracy in manual production processes.

The blended will provide Master's students the opportunity to deepen their knowledge of deep learning for time series data. Combining interactive lectures with collaborative projects, students will gain experience applying deep learning methods to production datasets from Tool Tracking smart sensors. The seminar will feature theoretical foundations, hands-on coding exercises, and algorithmic improvements tailored to real-world applications.

The structure is as follows:

 Face-to-face workshops: Alternating between FAU and PUCV, in a hybrid format, workshops will foster deeper engagement with seminar topics through in-person teaching and project presentations.
  

After completing the module, students

Syllabus