Machine Learning Mathematics Resources

September 2018 ยท 2 minute read

If you’re about to start your first Machine Learning course and wanted to know how much maths you’re gonna need, then good news! I’ve compiled a list of resources which I think will be useful in getting you up to speed, assuming you’ve studied lower undergraduate maths.

The list, inspired by Prof. Iain Murray’s own list, is split by each major subfield of mathematics that is required in machine learning, i.e. probability theory, linear algebra, and calculus.



Before you start, you might want to assess how badly you’ve forgotten all the maths you’ve put effort into learning not so long ago. So, first read through this cheat-sheet put together by Prof. Iain Murray:

By skimming through it, you’ll identify concepts and formulas you have forgotten or are not comfortable with. These will constitute your weak points. Now let’s strengthen those soft spots.




There is quite a lot of material to cover, so make sure to take plenty of breaks so that you do not lose focus and get bored. Do a bit every day, instead of cramming everything in a weekend. Good luck!