Lehr- und Forschungseinheit für Datenbanksysteme
print


Breadcrumb Navigation


Content

Deep Learning and Artificial Intelligence (WS 2018/19)

News

  • [09.11.2018] Since there was some confusion about the indices in the cross-correlation/convolution formulas, we provide some additional explanations in the file `crosscors.pdf' (see tutorial 4). In the updated lecture slides, the formula for full padding is used.
  • [09.11.2018] An updated version of the script on Convolutional Neural Networks with a correction of the convolution formula was uploaded. Please make sure you have the correct version.
  • [06.11.2018] The solution to the coding exercise of sheet 3 is now online.
  • [17.10.2018] Due to the amount of students actually visiting classes (about 25% of the registered master students), there is currently enough space in the classroom. Thus, we can reallow bachelor students to the course.
    Please, still keep in mind that this is still an advanced course which is not part of the bachelor curriculum and that prior knowledge in machine learning is still mandatory to understand the contents.
  • [16.10.2018] Given the current amount of registered students, we cannot allow bachelor students to participate.
  • [26.09.2018] Please note that "Machine Learning" or "Knowledge Discovery in Databases I" are essential in order to follow this lecture. If you have not yet visited these lectures, please listen those first and visit "Deep Learning and Artificial Intellience" next year. Thank you for your understanding.
  • [22.08.2018] Registration is open via Uniworx.

Organisation

  • Assistants: Sebastian Schmoll, 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

Time and Locations

All times are c.t. (cum tempore)

Component When Where Starts at
Lecture Wed, 13.00 - 16.00 h Room M 010 (HGB) 17.10.2018
Tutorial 1 Mo, 14.00 - 16.00 h Room E 006 (HGB) 22.10.2018
Tutorial 2 Mo 16.00 - 18.00 h Room E 006 (HGB) 22.10.2018

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

LectureTutorial
DateTopicDateTopic
17.10.2018 Introduction
Speaker: Sigurd Spieckermann
22.10.2018 Python Introduction (html, ex01.ipynb, py)
24.10.2018 Basic Neural Networks
Speaker: Florian Büttner
29.10.2018 Math Primer (ex02)
31.10.2018 Training Neural Networks
Speaker: Sigurd Spieckermann
05.11.2018 Computational Graphs and Vanishing Gradients (ex03, ex03.ipynb, ipynb_solution)
07.11.2018 Convolutional Neural Networks
Speaker: Denis Krompass
12.11.2018 Convolutional Neural Networks (ex04, ex04.ipynb, crosscors)
14.11.2018 Recurrent Neural Networks
Speaker: Pankaj Gupta
19.11.2018 RNNs (ex05),
CIFAR10 Competition (CIFAR10.ipynb)
21.11.2018 Deep Learning and Uncertainty
Speaker: Markus Geipel
26.11.2018  
28.11.2018 Representation and Distributional Learning
Speaker: Pankaj Gupta
03.12.2018  
05.12.2018 Deep Learning Tools
Speaker: Denis Krompass
10.12.2018  
12.12.2018 Generative Models
Speaker: Florian Büttner
17.12.2018  
19.12.2018 Sequential Decision Problems and autonomous Agents
Speaker: Matthias Schubert
07.01.2018  
09.01.2019 Modelfree Reinforcement Learning
Speaker: Matthias Schubert
14.01.2019  
16.01.2019 Value Function Approximation
Speaker: Matthias Schubert
21.01.2019  
23.01.2019 Policy Gradients and Actor Critic Learning
Speaker: Matthias Schubert
28.01.2019  
30.01.2019 Knowledge Graphs in AI
Speaker: Volker Tresp
04.02.2019  
06.02.2019 Q&A ----------------