About this course

Derivation and applications of advanced matrix methods for pattern recoginition, machine learning and data analysis: The subjects include clustering, projection- and matrix factorization methods, variable selection and regularization for regression- and classification problems. We will also cover efficient computations for model selection and -validation.

Learning outcome

The students will learn both the theoretical background and how to implement various methods for advanced analysis of research data.

  • Learning activities

    The teaching will be given as lectures, practical exercises and project work.

  • Syllabus

    Will be announced in the beginning of the semester.

  • Prerequisites

    Programming experience and MLA210or MATH280

  • Recommended prerequisites

  • Assessment method

    Oral exam based on the syllabus and mandatory project exercises.

  • About use of AI

    Exam: K1 - No use of AI.

    Oppgave: K2 - Specified use of AI. The use of AI technology to support the learning process in the course is recommended. The use of AI must comply with the guidelines for the use of artificial intelligence (AI) at NMBU.

    Descriptions of AI-category codes.

  • Examiner scheme

    The censor will evaluate the examination including questions about the mandatory project work.

  • Mandatory activity

    Mandatory project exercises throughout the semester. Rules for approval of mandatory activity will be presented at the start of the semester.

  • Notes

    The course is not taught in 2024

  • Teaching hours

    4 hours per week including exercises.