Entry requirements

120 credit points. Participation in Theoretical Foundations for Data Science. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

• have acquired a profound insight into some delimited area of mathematics for data science,
• have been introduced to some area of current mathematical research and be able to independently acquire information about literature and problems in the area,
• orientate themselves independently with regard to literature and issues in the field,
• be able to prepare and hold a seminar presentation in some area of modern mathematics for data science.

Content

The content of the course differs from time to time.

Examples:

• Network and queuing theory
• Asymptotic statistics
• Learning theory, decision theory and game theory
• Approximation theory
• Survival analysis with medical applications
• Brownian motion
• Solving partial differential equations or inverse problems using machine learning.
• Optimization theory
• Analysis of algorithms
• Mathematical logic for artificial intelligence

Instruction

Lectures and seminar sessions.

Assessment

Written assignments with oral follow-up examination (7,5 hp).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.