Registration

Register here

Lecture Course: Deep Learning for Graphics and Visualization, Summer Term 2019

Content

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.

Workshop

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.

Timetable

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

Lecturer

Prof. Dr Timo Ropinski

Dr Pedro Hermosilla Casajús

Alex Bäuerle

Michael Schelling

Patrik Puchert

Sebastian Hartwig

Dominik Engel

Further Information

When: 26. - 30. August 2019

Where: Ulm University, O27

20 participants, register here.

If possible, bring your notebook for the workshops.