MA-217 Statistical Machine Learning

The course is connected to the following study programs

• Industrial Mathematics, Bachelor's Programme
• Advanced Teacher Education level 8-13, 5-year Master's Programme

Teaching language

Norwegian or English

Recommended prerequisites

MA-166

A good understanding of linear algebra and calculus will be an advantage.

Course contents

Multiple regression. Non-linear transformations of data. Confidence intervals for estimation of parameters. Logistic regression. Support vector machines.

 

Learning outcomes

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

• the most important aspects of supervised learning

• build, estimate and interpret linear and non-linear models

• create and interpret basic models for classification of data

• use a programming language to apply machine learning techniques on large data sets

 

 

Examination requirements

Required assignments must be approved, see Canvas for more information.

Teaching methods

Lectures, work in small groups and compulsory assignments. Computer lab. If needed, the course is taught in English. The course has an expected workload of around 200 hours.

Evaluation

The person responsible for the course, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.

Offered as Single Standing Module

Ja

Assessment methods and criteria

A 5-hour written examination. Graded assessment.

Reduction of Credits

This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:

Course	Reduction of Credits
MA-202 – Statistics 2	7.5
MA-441 – Statistics for Primary School Teachers	7.5
MA-445 – Statistics for Secondary School Teachers	7.5
MA-225 – Statistical machine learning	7.5