Linear Algebra

Recommended Literature

The content of this linear algebra chapter is tailored to the needs for the following topics. If you want a more general and extensive overview, the following material is recommended.

• Linear Algebra and Optimization for Machine Learning by Charu C. Aggarwal Chapter one, in particular Sections 1.1-1.3 give a good introduction to vector spaces and matrices, norms and matrix multiplication. If you want to go deeper into the subject of linear algebra, then I would recommend to have a look at the Sections 2.1-2.4 and 7.1 and 7.2 as well.

• The Course Linear Algebra and Applications (2DBI00) from Michiel Hochstenbach Michiel is giving a very good course at TU/e about linear algebra and applications, where the applications are often data mining/machine learning problems. You can see the video lectures of 2018/2019 at the \href{http://videocollege.tue.nl}{videocollege}. Search for 2DBI00 in channels (not videos!). Select 2018-2019 to watch the latest recorded videos.