Statistical learning

Basic Information

M144 (2+0+3) - 7 ECTS credits

To acquaint students with concepts, methods and algorithms of statistical learning. Introduce abstract learning models and basic concepts of statistical learning theory. Cover the main methods of supervised and unsupervised learning such as decision trees, support vector machines and neural networks with special emphasis on data applications.

You can access the course content at the following link: PDF

Teachers

Instructor: Dr. Danijel Grahovac, Associate Professor
Supporting Instructor: Dominik Mihalčić

Basic literature

T. Hastie, R. Tibshirani, J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, New York: Springer, 2nd edition, 2016.
G. James, D. Witten, T. Hastie, R. Tibshirani. An introduction to statistical learning, New York: Springer, 2nd edition, 2021.

Additional literature

Teaching materials

The materials are available on the internal Teams channel of the course, through which all internal communication takes place. Students are required to register on the course’s Teams channel. The channel code for joining the course can be found in the schedule.