MLA210 Machine Learning applications in finance and technology
Showing course contents for the educational year 2022 - 2023 .
Course responsible: Ulf Geir Indahl
Teachers: Kristian Hovde Liland, Tor Kristian Stevik
ECTS credits: 10
Faculty: Faculty of Science and Technology
Teaching language: EN, NO
Teaching exam periods:
The course is implemented and censored each spring semester.
Course frequency: Each spring semester.
First time: Study year 2021-2022
Matrix- and least-squares methods for pattern recognition and data analysis applications in technology and finance. Some applications are: Time series analysis, document analysis, portfolio optimization, process control and muliti-objective optimization. The programming language Julia will be used for implementation and computations.
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..
Will be announced in the beginning of the semester.
Mandatory project exercises throughout the semester. Rules for approval of mandatory activity will be presented at the start of the semester.
Oral or written exam based on the syllabus and mandatory project exercises.
Type of course:
4 hours lectures per week. 2 hours exercise groups per. week
The sensor will evauate the exam answers including the mandatory project exercises.
Allowed examination aids: A1 No calculator, no other aids
Examination details: Written exam: Letter grades