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.
- The teaching will be given as lectures, practical exercises and project work..
- 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 - The sensor will evauate the exam answers.
- Mandatory project exercises throughout the semester. Rules for approval of mandatory activity will be presented at the start of the semester.
- 4 hours lectures per week. 2 hours exercise groups per. week