Lehr- und Forschungseinheit für Datenbanksysteme
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Deep Learning and Artificial Intelligence (WS 2018/19)

News

  • [11.01.2019] The description of exercise 10-2a and the solution for 10-2c were updated.
  • [07.12.2018] Exercise 07 (including solution) was updated due to notation clash.
  • [04.12.2018] The date for the final exam is Feb 21 at 10 a.m. Details see below.
  • [23.11.2018] Note that we made a small update regarding the notation in exercise 2 in exercise sheet 6. The latest version is now online.
  • [20.11.2018] To further support your learning process, we decided to provide the solutions to the tutorial exercises from now on. They will be published after the corresponding tutorial lesson and are password-protected. Everyone registered to the course should have received an Email with the credentials to access the solutions. Please let the assistants of the lecture know if you are taking the course but did not receive an Email.
  • [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 solutions of 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

Final Exam

When: Thu, Feb 21 from 10 a.m. to 12 p.m. s.t.
Where: Main Building M 218
Registration: Uniworx

Additional Exam

When: TBA
Where: TBA
Registration: Uniworx (TBA)

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
DateTopicDateExercisesSolutions
17.10.2018 Introduction
Speaker: Sigurd Spieckermann
22.10.2018 Python Introduction (ex01.html, ex01.ipynb, ex01.py)  
24.10.2018 Basic Neural Networks
Speaker: Florian Büttner
29.10.2018 Math Primer (ex02) ex02_sol
31.10.2018 Training Neural Networks
Speaker: Sigurd Spieckermann
05.11.2018 Computational Graphs and Vanishing Gradients (ex03, ex03.ipynb) ex03_sol, ex03_sol.ipynb
07.11.2018 Convolutional Neural Networks
Speaker: Denis Krompass
12.11.2018 Convolutional Neural Networks (ex04, ex04.ipynb) ex04_sol, crosscors
14.11.2018 Recurrent Neural Networks
Speaker: Pankaj Gupta
19.11.2018 RNNs (ex05),
CIFAR10 Competition (CIFAR10.ipynb)
ex05_sol
21.11.2018 Deep Learning and Uncertainty
Speaker: Markus Geipel
26.11.2018 Uncertainty (ex06, MCMC.ipynb, LSTM.ipynb) ex06_sol, MCMC_sol.ipynb, LSTM_sol.ipynb
28.11.2018 Representation and Distributional Learning
Speaker: Pankaj Gupta
03.12.2018 Representation & Distributional Learning (ex07, autoencoder.ipynb, RBM.ipynb) ex07_sol, autoencoder_sol.ipynb
05.12.2018 Deep Learning Tools
Speaker: Denis Krompass
10.12.2018 Tooling (ex08, torch.ipynb) ex08_sol, Autoencoder.py, AutoencoderKeras.py
12.12.2018 Generative Models
Speaker: Florian Büttner
17.12.2018 Generative Models (ex09, vae.ipynb) ex09_sol
19.12.2018 Sequential Decision Problems and autonomous Agents
Speaker: Matthias Schubert
07.01.2018 MDP (ex10, mdp.ipynb) ex10_sol
mdp_sol.ipynb
09.01.2019 Model-free Reinforcement Learning
Speaker: Matthias Schubert
14.01.2019 RL (ex11, rl.ipynb) ex11_sol
rl_sol.ipynb
16.01.2019 Value Function Approximation
Speaker: Matthias Schubert
21.01.2019 Function Approximation (ex12, QLearning.ipynb)  
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 ----------------