Applied Linear Algebra for Data Science, 7.5 credits

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 of Google, are 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.