Deep Learning and Artificial Intelligence (WS 2020/21)

News

Organisation

• Umfang: 3+2 hours weekly (equals 6 ECTS)
• Responsible Professor: Prof. Dr. Matthias Schubert

• Lecturers:

• Assistants: Michael Fromm, Niklas Strauß
• Required: Lecture "Machine Learning" or equivalent, Lecture "Knowledge Discovery in Databases I" or equivalent
• Audience: The lecture is directed towards Master students in Mediainformatics, Bioinformatics,  Informatics, and Data Science
• Registration: Please register on the Uni2Work site.

Final Exam

 

Additional Exam

 

Time and Locations

All times are c.t. (cum tempore)

Component	When	Where	Starts at
Lecture	Wed, 13.00 - 16.00 h	Room S 007 (Schellingstr. 3)	04.11.2020
Tutorial 1	Mo, 14.00 - 16.00 h	Room S 005 (Schellingstr. 3)	09.11.2020
Tutorial 2	Mo 16.00 - 18.00 h	Room S 005 (Schellingstr. 3)	09.11.2020

Content

During the last decade the availability of large amounts of data and the strong increase in computing power allowed a renaissance of neural networks and advanced planning techniques for independent agents. Whereas the area of deep learning extended well established neural network technology to allow a whole new level of data transformation, modern reinforcement learning techniques yield the artificial backbone for intelligent assistant systems and autonomous vehicles.

The course starts with an introduction to neural networks and explains the developments that led to deep architectures. Furthermore, the course gives an introduction to advanced planning techniques and how they can be trained using deep neural networks and other machine learning technologies.

Course Schedule

Lecture		Tutorial
Date	Topic	Date