AI@WWU - A Practical Introduction to AI Theory and Techniques for Interdisciplinary Research

Target group: University staff from all disciplines and career levels (doctoral students, PostDocs, Group Leaders, etc.)

AI and in particular machine learning (ML) tools become more and more accessible due to easy to use programming environments (esp. Python) and libraries (esp. Tensorflow and Pytorch). In order to apply these powerful tools for a variety of research projects, some basic understanding is required to tackle data preparation, visualisation and successful ML algorithm usage. In this course we will (1) teach AI and machine learning basics (70% of the course) and (2) apply these techniques to custom problems and custom data provided by the participants (30% of the course). In particular, we will introduce several state of the art deep learning algorithms like CNNs, LSTMs and Autoencoder. The entire course will be interactive and the participants will implement and use all presented techniques in pre-configured test environments.

Overview of content:

  • Python programming for machine learning (basics)
  • Theoretical and practical machine learning basics
  • State-of-the-art (deep) machine learning concepts
  • Advanced training concepts
  • Transferable machine learning skills (applying machine learning to own data; AI @own dataset)

Methods:  Presentations (20-30%) and practical e-learning training (70-80%; hands-on machine learning)

Trainer:
Prof. Dr. Benjamin Risse (responsible) and Lars Haalck

Anmeldung

AKTIONTERMINDozent*inORTfreie Plätze
AI@WWU - A Practical Introduction to AI Theory and Techniques for Interdisciplinary Research 0
Anmeldungen abgeschlossen 29.06.2020 bis 31.08.20 Benjamin Risse ZOOM Meeting, weitere Infos folgen 0