Linear Algebra and Learning from Data

The course can be considered as a complement of the linear algebra courses you have studied at our department, but at a more advanced level.

We'll pay more attention on how to abstract relevant mathematics and structures from applications for example data and how to apply the theory you've studied. The course will start with some elementary elements in linear algebra e.g. SVD, principal components, matrix norms, generalized eigenvalues and interlacing eigenvalues. In particular we'll deal with these topics in a numerical sounding way. Later we'll turn to an interactive treatment of linear algebra and subjects from data.

Course contents:

  • Basic computationally efficient algorithms for large matrices
  • Principal Component Analysis
  • Sparse and underdetermined systems and their relation to data compression
  • Construction of neural networks and models for deep learning
  • Fitting hyperparameters
  • Selected topics on particular types of matrices

The course consists of one element.


Teaching Format

Instruction consists of lectures, exercises, and computer projects.


Assessment

The course is assessed through written examination and hand-in exercises.

Examiner

A list of examiners can be found on

Exam information

The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.


Note that the course literature can be changed up to two months before the start of the course.

G. Strang: Linear Algebra and Learning from Data. Wellesley-Cambridge Press.

List of course literature Department of Mathematics

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Course web

We do not use Athena, you can find our course webpages on kurser.math.su.se.