[Course website | Script (under development)]
We present here some of the basics for quantum computation, using only linear algebra over the real numbers. No previous knowledge of quantum mechanics or analysis with complex numbers is required.
[Notes | Presentation]
How can you tell if your algorithm is learning correctly from your data? These notes present a method for this, using information theory. Originally proposed by Prof. Joachim Buhmann.
[Notes | Slides part 1 | Slides part 2 | Slides part 3]
Derivation of SVMs, with a preface on Lagrange multipliers, illustrated with a petrel, a cat, and a fish.
[Video]
An overview of Bayesian inference and maximum-likelihood, using a shoe shop as an example.
[Video]
A simple, but rigorous derivation of the expectation-maximization algorithm using a bidimensional dog and a vegan flea.
[Notes | Presentation | GIF]
A half-day workshop giving an introduction to classification, using machine and deep learning. Only basic programming knowledge in Python is required.
[Presentation ML | Presentation DL | Docker image ML | Docker Image DL]