Machine Learning for Business

About

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.

Fee info

Early Bird until April 15, 2025. The course fee covers tuition, course materials, daily lunches and coffee/tea during short breaks, social event including a dinner and farewell drinks. The course fee does not include accommodation.