Lehr- und Forschungseinheit für Datenbanksysteme

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Deep Learning and Artificial Intelligence (WS 2019/20)


  • [18.03.2020] Due to the corona pandemic the additional exam on 06.04 is canceled.
  • [12.03.2020] Following the instructions from the ministry, all teaching operations (i.e. exam inspections, exams, lectures, etc.) are suspended until further notice. That means that we can not make any definitive statements about whether or when additional exams/courses/exam inspections will take place. Please refrain from sending individual Emails asking for more information, as we don’t know how things will develop ourselves. We will keep you posted and let you know as soon as we get any new instructions from above. Please also refer to: https://www.uni-muenchen.de/aktuelles/corona_informationen/index.html . Thanks and sorry for the inconvenience!
  • [19.11.2019] The date for the final exam is now set: Feb 19 2020 at 4 p.m. (16:00 Uhr) Details see below.
  • Slides for the lecture, exercise sheets and other material will be published exclusively by https://uni2work.ifi.lmu.de/course/W19/IfI/DL&AI
  • [26.09.2019] The registration for the course via Uni2Work is now open: https://uni2work.ifi.lmu.de/course/W19/IfI/DL&AI


  • Assistants: Sabrina Friedl
  • 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

Final Exam

When: Wed, Feb 19 2020, 4 p.m. - 6 p.m. (16:00 -- 18:00 Uhr) sine tempore!>
Where: Main Building B 101
Registration: Uni2Work
Allowed aids: Two sheets of Din A4 paper with handwritten notes on both sides, calculator

Additional Exam

When: tbd
Where: tbd
Registration: tbd
Allowed aids: Two sheets of Din A4 paper with handwritten notes on both sides, calculator

Time and Locations

All times are c.t. (cum tempore)

Component When Where Starts at
Lecture Wed, 13.00 - 16.00 h Room S 004 (Schellingstr. 3) 16.10.2019
Tutorial 1 Mo, 14.00 - 16.00 h Room R 051 (Schellingstr. 3) 21.10.2019
Tutorial 2 Mo 16.00 - 18.00 h Room R 051 (Schellingstr. 3) 21.10.2019


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
16.10.2019 Introduction
Speaker: Sigurd Spieckermann
23.10.2019 Basic Neural Networks
Speaker: Yinchong Yang
30.10.2019 Training Neural Networks
Speaker: Sigurd Spieckermann
06.11.2019 Deep Learning Tools
Speaker: Denis Krompass
13.11.2019 Convolutional Neural Networks
Speaker: Denis Krompass
20.11.2019 Recurrent Neural Networks
Speaker: Pankaj Gupta
27.11.2019 Deep Learning and Uncertainty
Speaker: Florian Büttner
04.12.2019 Representation and Distributional Learning
Speaker: Pankaj Gupta
11.12.2019 Generative Models
Speaker: Yinchong Yang
18.12.2019 Sequential Decision Problems and Autonomous Agents
Speaker: Matthias Schubert
08.01.2020 Model-free Reinforcement Learning
Speaker: Matthias Schubert
15.01.2020 Value Function Approximation
Speaker: Matthias Schubert
22.01.2020 Policy Gradients and Actor Critic Learning
Speaker: Ma tthias Schubert
29.01.2020 Knowledge Graphs in AI
Speaker: Volker Tresp
05.02.2020 Q&A ----------------