FORK 1014 Preparatory Course in Mathematics for Data Science
FORK 1014 Preparatory Course in Mathematics for Data Science
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.
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.
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.
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.
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
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.

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.
Calculus & ideally some prior exposure to linear algebra.
Activity  Duration  Comment 

Teaching  9 Hour(s)  
Individual problem solving  18 Hour(s) 
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