Selection: Higher education credits in science and engineering (maximum 240 credits)
16 February 2023 – 27 March 2023
Entry requirements: 120 credits. Computer Programming II or Programming, Bridging Course. Linear Algebra II. Scientific Computing II or Scientific Computing, Bridging Course or Statistical Machine Learning. Proficiency in English equivalent to the Swedish upper secondary course English 6.
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application or tuition fees. Formal exchange students will be exempted from tuition fees, as well as the application fee. Read more about fees.
Application fee: SEK 900
Tuition fee, first semester:
Tuition fee, total:
About the course
The fields of data science and machine learning lean on many applications of linear algebra. Data are often represented in matrix form, and data are analysed through matrix and vector operations. If you would like to understand the relations between features, meaning understanding how columns depend on each other, it can be done with the algorithm QR factorisation.
To reduce the dimensionality of large data sets (transform a large set of variables into a smaller one that still contains most of the information) is in the data science world called Principal Component Analysis (PCA), but is really the same thing as Singular Value Decomposition. Again, it is really an algorithm in linear algebra. Ranking algorithms, like the Pagerank algorithm which formed the basis og Google, is really a form of an algorithm for finding eigenvalues. To really understand data science and machine learning, linear algebra is essential.
In this course we focus on numerical linear algebra, i.e. the computational methods and algorithms used in data science. We look at how data is stored, how computations are performed efficiently, and why the methods work.