HOW TO COPE WITH STRESS AND CHANGE TO FIT IN FUTURE ROLES

Artificial intelligence and machine learning for aviation applications


CONTENT_LANGUAGE:GENERIC_EN
Course description

This training module introduces students to Artificial Intelligence (AI) and Machine Learning (ML) and is intended for aviation professionals of all levels who would like to get a basic understanding of AI and ML while exploring some of the challenges associated with this technology and its applications in the aviation industry.
Prerequisites

There are no prerequisites for this module.
Objectives

At the end of the course the student will be able to:

  • Explain basic AI/ML concepts and techniques
  • Select appropriate AI/ML techniques to solve simple real-world problems 
  • Describe how AI/ML are being applied to aviation
  • Identify new applications of AI/ML in aviation
  • Explain the challenges associated with AI/ML in the context of aviation
  • Examine the impact of an AI/ML system on human-machine interaction
  • Discuss the pillars of trustworthy AI
Program

This course will cover the following themes:

  • Basic AI/ML concepts and terminology
  • Enablers of AI and ML
  • Capabilities of AI and ML
  • Types of ML (Supervised, Unsupervised and Reinforcement Learning)
  • Basic ML techniques and associated challenges
  • Current and emerging real-world applications of AI/ML in the aviation sector
  • Building blocks of trustworthy AI
Book

S. J. Russel and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Prentice Hall, 2020.

“Artificial Intelligence Roadmap: A human-centric approach to AI in Aviation,” European Union Aviation Safety Agency (EASA), Version 1, Feb. 2020. Accessed on June 2, 2022. [Online]. Available: https://www.easa.europa.eu/downloads/109668/en

“The FLY AI Report: Demystifying and Accelerating AI in Aviation/ATM,” European Aviation Artificial Intelligence High Level Group (EAAI HLG), March 2020. Accessed on June 2, 2022. [Online]. Available: https://www.eurocontrol.int/publication/fly-ai-report 
Exercises

The assessment exercises of this course comprise: a glossary; drag-and-drop exercises; discussion forums; a concept map; a case study; and multiple choice questions. Students will also be assessed on their level of participation. Most of the assessments will be graded.
Professor
Jason Gauci
Video professors
Prof. Jason Gauci -
List of video lessons