Mini Course 5

In linear algebra, eigendecomposition or sometimes spectral decomposition is the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors. 

Eigendecomposition has a huge number of applications in science: solving linear differential equations, image analysis, machine learning, quantum mechanics, statistics and more.

In this course, we will cover the basics of the technique and motivate it by introducing a number of scientific applications. This course will be designed in a flipped classroom method, where the theory will be introduced in short videos and the in-person sessions will cover exercises and applications.

Target audience

This course is suitable for anyone. There will be some programming for exercises, but participant experience will be expected to be low.

Teachers

We are currently looking for teachers. 

Program

The details of the course have not been set yet, but it will likely take place over 3 or 4 2-hour sessions in April or May.

Some hands-on exercises will involve some programming, most likely in Python, but expectations of participants experiences will be kept low.

More information

  • Location: B701, Computer Lab, Lab 3.
  • What to bring:
    • Note-taking material
    • A laptop
  • Zoom link: if you prefer joining remotely, or if B701 exceeds 50% capacity, you can join using this link. Unfortunately, we won't be able to provide much help with the hands-on part via Zoom. 
  • Video Recording: this course might be recorded and uploaded online, only the teacher will be recorded. Contact Jeremie Gillet if you have reservations about this.
  • Drinks: There will be free coffee and tea, bring your cup!

If you are interested in the course but cannot participate to this particular event, let us know and we will contact you for any later occurrence of this course.

Thank you very much for your interest.

 
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Have you studied Matrix Algebra and Eigendecomposition before? How comfortable are you with it? Do you use it in your research?
Why are you interested in this topic? Is there a particular thing you would like to learn?