FORK 1014 Preparatory Course in Mathematics for Data Science

FORK 1014 Preparatory Course in Mathematics for Data Science

Course code: 
FORK 1014
Department: 
Economics
Credits: 
0
Course coordinator: 
Eivind Eriksen
Course name in Norwegian: 
Preparatory Course in Mathematics for Data Science
Product category: 
Master
Portfolio: 
Master - Preparatory course
Semester: 
2024 Autumn
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

Linear Algebra is a branch of mathematics that is extremely useful in data science and machine learning, and vectors and matrices are the language used in almost all models. In this preparatory course, we give a quick review of the most fundamental concepts and tools from linear algebra. 

Learning outcomes - Knowledge

After completing the course, the student will have knowledge of the most fundamental mathematical concepts, models, theories, and methods from linear algebra, and how these mathematical models and methods can be used.   

Learning outcomes - Skills

After completing the course, the student will be able to do fundamental vector and matrix computations including matrix multiplication, transpose, inverse, solving linear systems, determinant, trace, inner products of vectors, eigenvectors and eigenvalues, linear independence. The student will also be able to use these tools to anlyze problems and set up and solve relevant mathematical models. 

General Competence

After completing the course, the student will be able to reflect upon central assumptions for the models and theories used, and critically assess if they are met in applications. The student will be capable of critical thinking. The student will be able to reflect upon the results obtained, and critically assess if they are reasonable.

Course content

Linear Algebra:

  • Matrix algebra including matrix multiplication, inverses, transposes
  • Vector algebra including inner products, span, linear independence and bases
  • Determinant, trace
  • Linear transformations including eigenvectors and eigenvalues 
  • Definiteness of matrices
Teaching and learning activities

The course is taught in the beginning of the autumn semester, and consists of lectures (9 hours). There will be problem sets that the students should work with after each lecture. 

Software tools
No specified computer-based tools are required.
Additional information

-

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Required prerequisite knowledge

Calculus & ideally some prior exposure to linear algebra.

Type of Assessment: 
None
Total weight: 
0
Student workload
ActivityDurationComment
Teaching
9 Hour(s)
Individual problem solving
18 Hour(s)
Sum workload: 
27

Text for 0 credits