Amsterdam, Netherlands

Machine Learning for Business

when 22 July 2024 - 26 July 2024
language English
duration 1 week
credits 3 EC
fee EUR 800

The course aims to minimize the gap between modern statistical theory and practical application in business environments, where many statistical tools are underutilized. The curriculum covers essential methods and techniques pertinent to business operations, ensuring that students grasp the core statistical principles. By the course's conclusion, students should be able not only to utilize those techniques, but also explaining their outcomes to a broader audience, including senior management. While we review the theory, this is mainly a practical course with ample time dedicated to hands-on exercises. The course predominantly uses case studies from the financial sector, but the techniques and methodologies taught are applicable across a diverse range of industries.

Course leader

Eran Raviv holds a PhD in econometrics at Erasmus University Rotterdam, a master’s degree in Applied Statistics (Tel Aviv University) and a master’s degree in Quantitative Finance (EUR). Currently he is a senior quantitative investment strategist at APG

Target group

The course is accessible for (research) master students, PhD students and post-docs as well as professionals. Students are expected to have a solid background in calculus, linear algebra, and classical statistics. Good familiarity with open source languages such as R or Python is a must.

Admission requirements
Students are expected to have a solid background in calculus, linear algebra, and classical statistics. Good familiarity with open source languages such as R or Python is a must.

Course aim

The course aims to minimize the gap between modern statistical theory and practical application in business environments, where many statistical tools are underutilized.

Credits info

3 EC
Participants who joined at least 80% of all sessions and hand in the assignment will receive a certificate of participation stating that the summer school is equivalent to a work load of 3 ECTS. Note that it is the student’s own responsibility to get these credits registered at their university.

Fee info

EUR 800: Early Bird for PhD and Master students (available until April 15) € 800
PhD and Master Students € 1.000
EUR 1500: Early Bird for Academics (incl. postdocs) and Professionals (available until April 15) € 1500
Academics (incl. postdocs) and Professionals € 2.000