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. The course is comprised of two modules.
Module 1 (4,5 hp): Theory and problem solving This module addresses fundamental statistical models, statistical learning and maximum likelihood estimation, with an emphasis on supervised learning. Several commonly used models are introduced, and their mathematical properties are discussed, for instance linear regression and classification models, neural networks, support vector machines, as well as models for unsupervised learning. Furthermore, evaluation and validation of models are addressed.
Module 2 (3 hp): Computer assignments This module addresses the implementation of commonly occuring machine learning models, as well as investigating their properties.
