How to get from high school math to cutting-edge ML/AI
Here’s a 4-stage roadmap.
I’ll start by briefly describing all the stages – and then I’ll go back to each stage for a deep dive where I fully explain the rationale and point you to resources that you can use to guide your learning.
- Stage 1: Foundational Math. All the high school and university-level math that underpins machine learning. All of algebra, a lot of single-variable calculus / linear algebra / probability / statistics, and a bit of multivariable calculus.
- Stage 2: Classical Machine Learning. Coding up streamlined versions of basic regression and classification models, all the way from linear regression to small multi-layer neural networks.
- Stage 3: Deep Learning. Multi-layer neural networks with many parameters, where the architecture of the network is tailored to the specific kind of task you’re trying to get the model to perform.
- Stage 4: Cutting-Edge Machine Learning. Transformers, LLMs, diffusion models, and all the crazy stuff that’s coming out now, that captured your interest to begin with.
Note that I’ve spent the past 5+ years working on resources to support learners in stages 1-2, and there is a general lack of serious yet friendly resources in those stages, so I’m going to be including my own resources there (along with some others).
