Machine Learning

The course addresses the fundamental mathematical and statistical methods and models used within the field of machine learning. Its purpose is to provide a mathematical foundation for advanced level courses in machine learning and artificial intelligence, as well as to introduce machine learning applications within academia and industry. Topics include data quality, multivariate linear and logistic regression, decision trees, random forests, neural networks, cross-validation, and unsupervised learning via PCA. Students will implement models using R or Python and learn to evaluate and compare their performance based on dataset characteristics.

Written exam (80%), practical assignments (20%)