Syllabus for Mathematical Topics in Data Science

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.