Lehr- und Forschungseinheit für Datenbanksysteme

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Machine Learning (SS 2019)


  • 17.06.2019: This week's tutorials will regularly take place. We are highly sorry for the delay in uploading exercise sheet 07
  • 07.06.2019: A proof of the invertibility of the matrix occuring in the analytical solution of the linear regression problem with regularization can be found under tutorial 3. Please note that a modified version of the identity matrix has to be used.
  • 17.05.2019: The final exam will be on August 6th, 2019 from 10am - 12pm. Further details will be announced on time.
  • 25.04.2019: Important - the tutorials will start on May 7th, resp., May 8th, due to the Labor Day on May 1st
  • 25.04.2019: The lecture will be recorded. You can find the videos here.
  • 28.02.2019: The registration for this course via UniWorx will be open from April 1st, 2019 onwards.


  • Course: 3+2 hours weekly (equals 6 ECTS)
  • Lecture: Prof. Dr. Volker Tresp
  • Assistants: Christian Frey, Sabrina Friedl
  • Required: Professional skill of at least one programming language
  • Audience: The course is directed towards master students in informatics, bioinformatics and media informatics
  • Course Language: English

Time and Locations

All times are c.t. (cum tempore)

Component When Where Starts at
Lecture Thu, 9:00 - 12:00 h Room M 218 (Geschwister-Scholl-Platz 1) 25.04.2019
Tutorial 1 Tue, 14:00 - 16:00 h Room B 015 (Geschwister-Scholl-Platz 1) 07.05.2019
Tutorial 2 Tue, 16:00 - 18:00 h Room B 015 (Geschwister-Scholl-Platz 1) 07.05.2019
Tutorial 3 Wed, 14:00 - 16:00 h Room B 015 (Geschwister-Scholl-Platz 1) 08.05.2019
Tutorial 4 Wed, 16:00 - 18:00 h Room B 015 (Geschwister-Scholl-Platz 1) 08.05.2019


Machine Learning is a data-driven approach for the development of technical solutions. Initially motivated by the adaptive capabilities of biological systems, machine learning has increasing impact in many fields, such as vision, speech recognition, machine translation, and bioinformatics, and is a technological basis for the emerging field of Big Data.

The lecture will cover:

  • Supervised learning: the goal here is to learn functional dependencies for classification and regression. We cover linear systems, basis function approaches, kernel approaches and neural networks. We will cover the recent developments in deep learning which lead to exciting applications in speech recognition and vision.
  • Unsupervised Learning: the goal here is to compactly describe important structures in the data. Typical representatives are clustering and principal component analysis
  • Graphical models (Bayesian networks, Markov networks), which permit a unified description of high-dimensional probabilistic dependencies
  • Reinforcement Learning as the basis for the learning-based optimization of autonomous agents
  • Some theoretical aspects: frequentist statistics, Bayesian statistics, statistical learning theory

The technical topics will be illustrated with a number of real-world applications.


Course Schedule

25.04.2019 Introduction rec01 30.04.2019
No tutorials
---Labor Day May 1st---
02.05.2019 Perceptron (update!), Linear Algebra, Linear Regression (update!) rec02 07.05.2019
Exercise Sheet 01,
09.05.2019 Linear Regression (cont.), Basis Functions, Neural Networks rec03 14.05.2019
Exercise Sheet 02,
ex02_pytorch.ipynb, ex02_pytorch_solution.ipynb
16.05.2019 Neural Networks (cont.), Deep Learning rec04 21.05.2019
Exercise Sheet 03,
ex03_pytorch.ipynb, ex03_pytorch_solution.ipynb
23.05.2019 Deep Learning (cont.) rec05 28.05.2019
Exercise Sheet 04,
ex04_pytorch.ipynb (updated!) ex04_pytorch_solution.ipynb
30.05.2019 No lecture: Ascension Day --- 04.06.2019
Exercise Sheet 05,
ex05_pytorch.ipynb, ex05_pytorch_solution.ipynb
06.06.2019 Kernels rec06 11.06.2019
Exercise Sheet 06
13.06.2019 Kernels (cont.), Manifolds rec07 18.06.2019
Exercise Sheet 07
20.06.2019 No lecture: Corpus Christi --- 25.06.2019
27.06.2019 02.07.2019
04.07.2019 09.07.2019
11.07.2019 16.07.2019
18.07.2019 23.07.2019
25.07.2019 --- ---
06.08.2019 Final Exam


The lecture will be recorded. The videos will be uploaded after each lecture and can be found here

Final Examination

  • Date: August 6th, 2019
  • Time: 10am - 12pm
  • Location: Audimax (A030), B 201
  • Registration will be via UniWorx (For students with no UniWorX account it is mandatory to write an email to the assistants such that we can register you manually)
  • Guidelines: tba

Please note: There will be no second exam!

Recommended Literature:

  • Deep Learning. Ian Goodfellow, Yoshua Bengio and Aaron Courville
  • The Elements of Statistical Learning: Data mining, Inference and Prediction. Hastie, Tibshirani, Friedman
  • Machine Learning: a Probabilistic Perspective. Kevin Murphy
  • Bayesian Reasoning and Machine Learning. David Barber
  • Pattern Recognition and Machine Learning. Bishop
  • Artificial Intelligence: A Modern Approach. Russel and Norvig

Links to Tutorials