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MLA210 Machine Learning With Examples From Technology and Finance

Credits (ECTS):10

Course responsible:Ulf Geir Indahl

Campus / Online:Taught campus Ås

Teaching language:Norsk

Course frequency:Each fall semester from the autumn of 2025 (the subject is not offered in spring 2025).

Nominal workload:250 hours

Teaching and exam period:The course is implemented and censored each fall semester.

About this course

Linear algebra and matrix methods are foundational to the fields of Machine Learning (ML) and Artificial Intelligence (AI), providing the mathematical framework necessary for many algorithms and processes. MLA210 focuses on Matrix and Least Squares Techniques for Pattern Recognition and Data Analysis with Examples of Applications in Technology and Economics. Some applications are: Time series analysis, document analysis, portfolio optimization, process control and muliti-objective optimization. The programming language Julia is used for implementation and computations.

Learning outcome

The students will learn both the theoretical background and how to implement the methodology for analysing data of real world applications.
  • Learning activities
    The teaching will be given as lectures, practical exercises and project work..
  • Prerequisites
    MATH113/131, MATH111/100og INF120 or similar courses in mathematics and programming.
  • Recommended prerequisites
    STAT100, DAT110or a similar introduction course in statistics.
  • Assessment method
    Oral or written exam based on the syllabus and mandatory project exercises.

    Oral exam Grading: Letter grades Written exam Grading: Letter grades Permitted aids: A1 No calculator, no other aids
  • Examiner scheme
    The sensor will evauate the exam answers.
  • Mandatory activity
    Mandatory project exercises throughout the semester. Rules for approval of mandatory activity will be presented at the start of the semester.
  • Teaching hours
    4 hours lectures per week. 2 hours exercise groups per. week