Deep Learning for Multi-Model Systems

The workshop will take place tentatively on August 04, 2022, on Zoom and Youtube Livestreaming

About The Data Science Summer School

The Data Science Summer School is a series of theoretical and practical workshops on the exciting methods and technologies currently employed by industry, government, and civil society to address the world's most complex problems today. It is organized by the Hertie School Data Science Lab with funding and support from the Hertie School.

Workshop Details

Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years. The goal of multimodal deep learning (MMDL) is to create models that can process and link information using various modalities. Despite the extensive development made for unimodal learning, it still cannot cover all the aspects of human learning. Multimodal learning helps to understand and analyze better when various senses are engaged in the processing of information. This workshop will deliver a fundamental understanding and application of this new approach in deep learning methodology.

[Syllabus to be updated soon]


Christian Arnold
Prof. Christian Arnold

Prof. Christian Arnold is a Senior Lecturer at Cardiff University. Using data driven methods from statistics and machine learning, his work lies at the intersection between social science and computer science. His substantive research focuses on institutions in governance. What drives and determines the rules of political decision making? Prior to joining Cardiff, he had a position at Oxford University and worked as a Data Scientist in industry. He is a member of the academic advisory board for the Government Statistical Service at the Office for National Statistics. His focus there is on all matters related synthetic data.

Schedule (Central European Summer Time - CEST)

Session Starts

Deep Learning for Multi-Model Systems (Part I)

Short Break

Session Continues

Deep Learning for Multi-Model Systems (Part II)

Session Ends