Deep Learning – Winter Term 2026/27

This is the website for the Deep Learning lecture at the University of Tübingen in the Winter term 2026/27.

When, where, how

Both the lecture and the tutorial will be held in-person.

  • Lecture: Wednesday 14:15–15:45, Hörsaal N05, Hörsaalzentrum Morgenstelle.
  • Tutorial: Wednesday 16:15–17:45, Hörsaal N05, Hörsaalzentrum Morgenstelle.
  • Lecturer: Sebastian Bordt.
  • First lecture: 14.10.2026
  • First tutorial: 14.10.2026
  • Credits: 6 ECTS.

There will be no lecture / tutorial on 18.11.2026 and on 09.12.2026.

Team

Qualification Goals

In this course, students gain an understanding of the theoretical and practical concepts of deep neural networks, including optimization, inference, architectures and applications. After the course, students should be able to develop and train deep neural networks, reproduce research results and conduct original research in this area.

Overview

Each week consists of a lecture followed by a tutorial. The lecture introduces new material; the tutorial is hands-on and covers live-coding sessions, quizzes, old exam exercises, and programming tips and tricks. There is a 30-minute break between the two sessions (15:45–16:15).

  • Lecture: new material, examples and discussion.
  • Tutorial: a new exercise sheet is handed out and introduced, or the solutions to the previous one are discussed.
  • Helpdesk: once per week, where our TAs provide individual support. The time will be fixed in the first week and announced here.
  • Forum: we use the Ilias forum for questions about the lecture and the exercises. You are also welcome to ask in person in the break after the lecture.
  • Exam: a written exam at the end of the semester.

Prerequisites

Basic programming skills (Python) and a solid background in linear algebra and probability. Prior exposure to machine learning is helpful but not strictly required.

Registration

There is no mandatory registration for the course. If you want to take the class, simply attend the first lecture.

We do ask you to join the course in Ilias, since this is how we send announcements about the class – if you are not in the Ilias course, you will not receive them.

Registration for the exam is separate and goes through Alma. The exam date will be announced here.

Exercises

There are six exercise sheets over the semester. Each sheet runs for two weeks; the first one is handed out in the first week. The sheets contain pen-and-paper and coding tasks.

  • Nothing has to be handed in. We publish the solutions and discuss them in the tutorial.
  • You can work in groups of up to 4 students.
  • The sheets are highly relevant to the exam – that is the reason to do them.

Schedule

Lectures and tutorials take place on Wednesdays. Slides and exercise sheets will be made available in Ilias.

Date Lecture Exercise
14.10.2026 Introduction Intro to EDF
21.10.2026 Computation Graphs Intro to EDF
28.10.2026 Deep Neural Networks I Classification
04.11.2026 Deep Neural Networks II Classification
11.11.2026 Regularization Regularization
18.11.2026 no lecture and no tutorial –
25.11.2026 Optimization Regularization
02.12.2026 Convolutional Neural Networks CNNs & Sequence Models
09.12.2026 no lecture and no tutorial –
16.12.2026 Sequence Models CNNs & Sequence Models
23.12.2026 – 06.01.2027 Christmas break –
13.01.2027 Transformers Transformers
20.01.2027 Scaling Transformers
27.01.2027 Image Generation Diffusion
03.02.2027 Image Generation –

Questions?

During the semester, please use the Ilias forum – questions asked there help everyone. Only in case of urgent questions, please contact Sebastian Bordt.