Lectures at ETH and UZH

Statistical learning theory 2020-2021

[Course website | Script (under development)]

Advanced machine learning 2020-2021

Course website

An introduction to machine learning for medicine 2020-2021

[Course description | Slides]

Other

An elementary introduction to quantum computing (VMI retreat 2022)

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]

Algorithm validation via information theory (Statistical learning theory 2020)

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]

Support vector machines (Advanced machine learning 2019)

Derivation of SVMs, with a preface on Lagrange multipliers, illustrated with a petrel, a cat, and a fish.
[Video]

Bayesianism, frequentism, and maximum-likelihood estimators (Advanced machine learning 2019)

An overview of Bayesian inference and maximum-likelihood, using a shoe shop as an example.
[Video]

A dog, a vegan flea, and the EM algorithm (Statistical learning theory 2019)

A simple, but rigorous derivation of the expectation-maximization algorithm using a bidimensional dog and a vegan flea.
[Notes | Presentation | GIF]

The essentials of machine and deep learning (Software crafters 2018)

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]