A brilliant introductory course on machine learning (mathematical perspective) (simulation + implementation)

Question

I am a grad student with a relatively good understanding of stochastic analysis / probability theory, but only basic coding experience.

What is a good source (textbook or lecture notes) for an introduction to machine learning? This will be for self-study.

*Importantly it would be great if the course:

1. Has video lectures accompanying the material.
2. Is integrated with Python or MATLAB so that I can get hands on experience.
3. Has quite a large focus on neural nets and Bayesian inference.

Answer 1

I'd recommend Dmitry Kobak's lecture series on "Introduction to Machine Learning" which is available on YouTube. You could also take Prof. Andreas Geiger's course on "Deep Learning" where neural networks is the main theme of the course. Or you could take Philipp Hennig's course on "Probabilistic Machine Learning" also on YouTube.

These courses are taught at the University of Tübingen and I think the courses are geared towards graduate students. The courses include the lecture videos and slides, coupled with exercises and practicals that are implemented in Python.

You could check out their YouTube channel and scroll through the courses to find what you think it's best for you at the moment.

Here's the link to their channel: https://m.youtube.com/@TubingenML/playlists