Time only SS
Time Tuesday, 11.45h-13.15h
Room 137
Credits 2 SWS / 5 ECTS
Exam Paper and Presentation


  • First lesson in term SS 20: 21.04.2020

Important notice for the summer term 2020: The course will initially be offered as a synchronous distance learning course during the SARS-CoV-2-related restrictions in term SS 20. I will post the corresponding Zoom link to the group of online registered users in the Persönlicher Stundenplan before 20.04.2020. The timetable according to the Starplan will apply. If or when we may meet again in lecture halls, the room indicated in Starplan will apply.

Prerequisites for participation

  • Successful completion of the CSM Machine Learning Lecture

Resources and Presentations


Theory Part

Based on the theory and algorithms, which have been taught in the Machine Learning Lecture, this seminar focuses on very recent Deep Learning algorithms for

  • Text
  • Images

Concerning text, the currently hottest topic in NLP are deep pretrained models, which provide contextual embeddings and can be fine-tuned applied for many NLP downstream tasks, such as Named Entity Recognition, Question-Answering, Automatic Text Generation, Natural Language Inference, Text Classification, etc. BERT, GPT-2 or XLNET are the most prominent representatives of this class of transformer models. In order to understand the theory and the applications of these models this lecture provides key-insights in

  • modelling of texts, words and characters (embeddings)
  • attention and self attention in neural networks
  • transformer models

Concerning image, this lecture focuses on Generative Adversarial Networks (GANs). The theory and architecture of the most important GANs are highlighted, and their applications, e.g.

  • automatic generation of photorealistic images,
  • image transformation and style transfer,
  • image modification, e.g. changing the emotion in a human’s face
  • super resolution

are demonstrated.

This part of the lecture will be stretched over the approximately first 5-6 weeks of the term. Exercises on the contents of this theory part will be provided. These exercises must be solved and submitted to the lecturer. The submission of the solved exercises is a prerequisite for passing the examination (examination is the project of the group work part)

Group Work Part

In the second part of the term, student-groups select one current topic of Deep Learning and analyse this topic in depth. A corresponding paper, tutorial, demo, etc. has to be prepared. These final project are presented by the student-groups at the end of the term.

List of potential topics to investigate (from SS 2019)

Deep Neural Networks for Language Understanding:

Explainable AI and Causality

Style Transfer Networks

Low-Resource-Learning / Transfer Learning / Active Learning

AlphaGo / AlphaZero / AlphaStar