AI and Data Science

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° ASU28 EN

In sports, more and more data has been accumulating from different sources such as sensors and videos. In this course you will get a chance to become an Olympian of Machine Learning applied to sports and learn how to harness the power of all that data. Join us in this cross-disciplinary journey between the sports and data science field!

Description

Sports has seen an increase in data produced, primarily due to tracking and wearable sensors. One might assume that a rise in data and metrics in sports would help athletes and coaches to support decisions - But this is hard said than done, as currently, there is an overwhelming amount of data to analyse. In other application areas, Machine learning methodologies have been employed successfully to help practitioners with these problems. Such examples can be seen in preventive maintenance, recommendation systems and others. In many of these applications, domain and technical expertise were required. Machine learning in sports is a relatively new field and holds big potential for innovation. This summer school aims to accelerate the development of skills of the participants necessary to develop machine learning models and methodologies applied to sports through a cross-disciplinary approach. At the end of the course, you will become an Olympian in Sports Analytics!

Program

Becoming an Olympian in Sports Analytics is a practical course that brings together sports and data scientists to learn and discuss how to harness the power of data in sports. With this course we want to:

Build knowledge by bridging the gap between sports science, computer science and the sports industry.

Create an international network of researchers and practitioners in advanced sports analytics that opens up opportunities for cross-pollination between the two fields.

The course will provide the right mix of introductory topics bringing up to speed all the participants, theoretical lectures and a practical real-life sports data project throughout the course.

Remarks

3 ECTS credits can be awarded upon successful completion of the programme. All certificates of completion are issued as a micro-credential.

To include the credits in the curriculum at the home institution, participants need an agreement with the responsible person at their university.

A certificate will be awarded at the end of the programme. In order to receive a certificate of completion, it is necessary that the participants complete an individual assignment after the course.

Course number:
ASU28
Type:
Short- en long-term programmes
Area of interest:
AI and Data Science, Health Sciences
Language:
EN
Academic year:
2021 - 2022 copy
Starting date:
05.09.2022
Contact person:
bosa@uantwerpen.be
Location

Stadscampus, University of Antwerp, Belgium

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