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.
A Mathematical Introduction to Machine Learning, 7.5 credits
Autumn Term 2024
The information below is only for exchange students
Starts
1 November 2024
Ends
19 January 2025
Study location
Umeå
Language
English
Type of studies
Daytime,
50%
Required Knowledge
The course requires courses in Mathematics, minimum 60 ECTS or at least two years of university studies and both cases require courses in linear algebra, multivariate calculus, mathematical statistics and computer programming, or equivalent. Proficiency in English and Swedish equivalent to the level required for basic eligibility for higher studies.
Selection
Students applying for courses within a double degree exchange agreement, within the departments own agreements will be given
first priority. Then will - in turn - candidates within the departments own agreements, faculty agreements, central exchange
agreements and other departmental agreements be selected.
Application code
UMU-A5803
Application
This application round is only intended for nominated exchange students. Information about deadlines can be found in the e-mail instruction that nominated students receive.
The application period is closed.
