Whether you realize it or not, lambda calculus has already impacted your world as a data scientist or a developer. If you’ve played around in functional programming languages like Haskell or F#, then you are familiar some of the same ideas. In fact, AWS’s serverless product is named Lambda after this branch of mathematics.

Watch this video to learn about lambda calculus.

Normally, I say the best way to get started with machine learning is to do machine learning. So, strictly speaking, there’s no “required math” to get started and follow along with online tutorials. However, if you want to take your AI game to the next level, then it’s time to get deep into the math.

Data Science Central outlines specifics around the subjects to tackle and where to find them online.

  1. Linear Algebra — Professor Strang’s textbook and MIT Open Courseware course are recommended for good reason. Khan Academy also has some great resources, and there is a helpful set of review notes from Stanford.
  2. Multivariate Calculus — Again, MIT Open Courseware has good courses, and so does Khan Academy.
  3. Probability — Stanford’s CS 229, a course I’ve mentioned later, has an awesome probability review worth checking out.

A lot has changed since I earned my degree in Computer Science, but the fundamentals like math and set theory have remained relatively constant. Ironically, I was student who loathed math for most of my academic career or at least I thought I did. I enjoyed Discrete Mathematics and Set Theory.

In truth, Computer Science majors have to learn a different kind of math compared to most other majors (except of course math majors). These branches of math are critical for those looking to go into research in fields like computer science, AI, or even pure mathematical research.

In this talk given at the Royal Institute, Eugenia Cheng explores how anyone can think like a mathematician to understand what people are really telling us – and how we can argue back.

Taking a careful scalpel to fake news, politics, privilege, sexism and dozens of other real-world situations, she will teach us how to find clarity without losing nuance.

Unlike traditional software development, as you progress further in data science and AI, you will encounter more and more advanced mathematics. Given the sad state of how math is taught in schools today (at least in the US), learning math quickly can dramatically impact the quality of your life and your career options.

Fortunately, Siraj Raval has got our back and, in this video, he offers up tips on how to learn math more quickly.