Machine Learning for Business (confirmed)

July 22-26 July, 2024 in Amsterdam, Zuidas 

Faculty

Eran Raviv holds a PhD in econometrics from Erasmus University Rotterdam, a master’s degree in applied statistics from Tel Aviv University and a second master’s degree in quantitative finance from Erasmus University Rotterdam. His research has been published in high impact peer-reviewed journals.

Meet the Lecturer

Course

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.

Topics covered

• Simulation, bootstrapping and subsampling for inference
• Advanced covariance estimation
• Density and quantile estimation
• Prediction models (Basics or neural networks, tree-based models)
• Dimension reduction methods (PCA, autoencoding)
• Outlier detection techniques
• Communicating machine learning outcomes to a non-technical audience

Literature

• Efron, Bradley. "Bootstrap methods: another look at the jackknife." In Breakthroughs in statistics: Methodology and distribution, pp. 569-593. New York, NY: Springer New York, 1992.
• Jolliffe, Ian T., and Jorge Cadima. "Principal component analysis: a review and recent developments." Philosophical transactions of the royal society A: Mathematical, Physical and Engineering Sciences 374, no. 2065 (2016): 20150202.
• Ledoit, Olivier, and Michael Wolf. "Honey, I shrunk the sample covariance matrix." UPF economics and business working paper 691 (2003).
• LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521, no. 7553 (2015): 436-444.

Schedule

The program spans a total of five days, commencing daily at 10:00 AM. The initial segment of each day is dedicated to 2.5 hours of theoretical instruction. Following a one-hour lunch break, the session resumes with a practical application phase, during which participants will practice and apply the concepts covered in the theoretical session, scripting in both R and Python.

Level

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.