It is hard to name a sector that will not be dramatically affected by machine learning or even artificial intelligence. Many excellent courses teach you the mechanics behind these innovations, helping you to develop an engineering skill set. This course takes a different approach. It is aimed at people who want to deploy these tools in business or policy, whether through start-ups or within larger organisations. While this requires some knowledge of how these tools work, it is only a tiny part of the equation — just as knowing how an engine works is a small part of understanding how to drive. What is needed is an understanding of what these tools do well and what they do poorly. This course focuses on giving you a functional, rather than mechanical, understanding. By the end, you should be an expert at identifying ideal use-cases and be well-equipped to improve analysis and policy using machine learning.
This course thus aims to give an overview and a grasp of popular Machine Learning (ML) techniques. Within this, you will be provided with an understanding of the opportunities and limitations of said techniques and a chance to interact with the experts. In practical terms, you will work hands-on with ML/AI methods in Python and demystify the black boxes to continue learning by yourself. Furthermore, the course will also present and discuss current policy and industry practice at European and international levels.
Measure and evaluated sustainability, accuracy, fairness and explainability of AI (SAFE)
Analyse and compare the difference between AI and sample regressions models
Develop database for AI and ML applications
Estimate decision tree and random forest models
Implement Natural Language Processing applications
Discuss ESAs and NCAs SupTech best practices and case of applications