These are my personal notes which are broadly intended to cover the basics necessary for data science, machine learning, and artificial intelligence. They have been collected from a variety of different sources, which I include as references when I remember to - so take this as a disclaimer that most of this material is adapted, sometimes directly copied, from elsewhere. Maybe it's better to call this a "remix" or "katamari" sampled from resources elsewhere. I have tried to give credit where it is due, but sometimes I forget to include all my references, so I will generally just say that I take no credit for any material here.
Many of the graphics and illustrations are of my own creation or have been re-created from others, but plenty have also been sourced from elsewhere - again, I have tried to give credit where it is due, but some things slip through.
Data science, machine learning, and artificial intelligence are huge fields that share some foundational overlap but go in quite different directions. These notes are not comprehensive but aim to cover a significant portion of that common ground (and a bit beyond too). They are intended to provide intuitive understandings rather than rigorous proofs; if you are interested in those there are many other resources which will help with that.
Since mathematical concepts typically have many different applications and interpretations and often are arrived at through different disciplines and perspectives, I try to explain these concepts in as many ways as possible.
Some caveats:
The raw notes and graphics are open source - if you encounter errors or have a better way of explanining something, please don't hesistate to submit a pull request.
~ Francis Tseng (@frnsys)