Machine Learning

The course provides a comprehensive introduction to the most important machine learning models and algorithms and their applications in finance, with an emphasis on model performance, validation, and interpretability.

At the end of the course, the students will have a good understanding of the most important (supervised and unsupervised) machine learning algorithms and their applications. They will know which models are suitable for a given problem and data set, how to evaluate the model quality, and how to interpret the results. In addition, they will have implemented a number of models using large sets of financial data.

This course is required for all participants. It is taught in the second semester and carries 6 ECTS credits. Go to the next course or see the list of courses.

  • Essential Literature
  • C. M. Bishop. Pattern Recognition and Machine Learning, Springer (2006).
  • T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Second Edition, Springer (2009).
  • S. Raschka, Python Machine Learning, Packt Publishing Ltd. (2015).
  • I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press (2017).
  • M. Lopez de Prado, Advances in Financial Machine Learning, John Wiley & Sons (2018).