In­tro­duc­tion to Ma­chine Learn­ing for Eco­nom­ics

Course content

In the current environment where data abounds of different complexity, it is crucial to extract and represent knowledge from business data. In Data Science, rational business decisions are made after harnessing different sources of data. Typical examples are credit scoring, bankruptcy prediction, fraud detection, customer loyalty, recommender systems, and revenue management. Building on theories of Supervised and Unsupervised Learning, this course aims to enhance your ability to apply Data Mining and Visualization tools for harnessing data. You will be exposed to the mathematical optimization models behind many of these tools, and the advantages that this mathematical modelling bring. The course uses computer software to illustrate how to apply the methodologies introduced.

The course’s development of personal competences:

During the course, and through a hands-on approach supported by Supervised and Unsupervised Learning theory, students will develop quantitative as well as mathematical modelling skills needed for Data Driven Decision Making. In addition, students will learn to appreciate the importance of using the right visualization tool to report final results.