Machine Learning can be found in almost all fields of computer science. This course teaches basic concepts of machine learning and how they are applied to computer graphics. This course covers the whole process of developping, training neural nets and also adapting complex models to new datasets. Learning from 3D points aka. point clouds as it is covered in this course, is a current research topic in the field of computer graphics. Students will thus first learn how to solve standard machine learning problems, before applying their know how to 3D data. All practical realizations will be made in Tensorflow, which is also introduced in the course.

We assume previous knowledge in computer science, but not necessarily in machine learning.

In the workshops we provide sample code and introduction to TensorFlow. For each chapter we prepare simple tasks which teach you how to implement the most basic parts of machine learning in TensorFlow using Python.

Goal of the workshop is that you can start writing your own code in order to train nets and adapt to your specific problems / data sets.

You are welcome to bring your own notebook for coding during the workshop. We will use the google colab platform to have enough compute power provided by google cloud servers.

Mon	Tue	Wed	Thu	Fri
Introduction	Intro: Tensorflow 2.0	Advanced Architectures	Generative Models	Unstructured CNNs
Multi Layer Perceptrions	Convolutional Neural Nets	Workshops	Workshops	Visual Architecture Inspection
	Workshops			Workshops

Access to Ulm University

• Timely Information and Maps of Ulm University

Shortcut to:

• Campusnavigator (Maps of bus stops, parking lots, and buildings (German))
• Hörsaalfinder (Tool to find a room)

Selection of suggested accomodation

• Hotel Stern (upper middle class standard)
• Ibis Budget Ulm
• Hotel Garni Lehrer Tal
• InterCityHotel (directly at main station)