Video links and example notebooks for WDSS's workshop 'Data-Driven Linear Algebra'.
Linear algebra is an incredibly powerful tool for solving a wide range of theoretical and practical problems. That said, despite the modern economy becoming further powered data each day, little time is given in undergraduate degree courses to the data-centric applications of linear algebra.
In this three-part workshop series, we will introduce a linear algebraic technique known as the singular value decomposition (SVD). This is likely a new concept to many undergraduate maths students, yet due to its importance in a data-driven economy, is starting to find its way into many introductory courses (e.g. the latest iteration of MIT's introductory course).
The sessions break down the topic into three parts:
- The motivation and theory behind the SVD
- Practical approaches for computing the SVD
- Application of the SVD to imaging/signal-processing/regression problems using Python/MATLAB
The sessions will be accessible to anyone with a basic (A Level) knowledge of matrices and no programming experience will be required.
- Session One Lecture Notes
- Session Two Lecture Notes
- Session Three Lecture Notes & Example Notebook