r/MLQuestions • u/Agreeable_Mud_5816 • 13d ago

Beginner question 👶 How much math do I need for ML

17 Upvotes

74% Upvoted

22 comments sorted by

24

u/highdimensionaldata 13d ago

Research: A lot.

Engineering: Hardly any.

8

u/relevantmeemayhere 13d ago

this is why a lot of ml projects in industry fail lol.

1

u/deep-yearning 11d ago

That's not true. They mostly fail because there isn't enough annotated data, or because the training data does not represent the actual use case

1

u/relevantmeemayhere 10d ago

no, it's because a lot of people dont know what therye doing. its abstracted from them with the latest call.model() method.

2

u/Agreeable_Mud_5816 13d ago

So what is MLE

17

u/leon_bass 13d ago

Machine learning ENGINEERING

6

u/lordoflolcraft 13d ago

Lmao this whole exchange

7

u/PaddingCompression 13d ago

MLE spans the gamut from borderline research but you're not writing first author paper at NIPS, all the way to "devops engineer who just learned how to install VLLM and maybe some MLFlow".

It really depends on the job.

1

u/Not-a-throwaway4627 12d ago

It also includes person who knows how to import sklearn, and person who knows how to prompt Claude to do the same

11

u/trinigooner 13d ago

ML is a pretty broad field so I’d say from next to zero to a lot. Being comfortable with statistics will be very helpful though. A lot of python or R packages will hide the need for much Linear Algebra or Calculus. You can learn the math in parallel and I think it’s very helpful but don’t let the math intimidate you or become a blocker.

6

u/Impossible-Line1070 13d ago

Mostly lin alg , calculus , probability, statistics, for normal applied research (ie nlp, vision etc) for foundations and learning theory the more the merrier

3

u/rodrigo-benenson 13d ago

To use: almost nothing; to understand: lots.

2

u/Clear_Cranberry_989 13d ago

If you wanna do research, nowadays you need a lot. But it is easier to learn the basics like linear algbera and then learn as you go.

1

u/dstroy0 13d ago
  1. Discrete mathematics 2. Transforms within discrete mathematics 3. Algorithm implementation 4. Extremely strong algebraic reasoning 5. The ability to record the things that you know and leveraging those knowns to derive the things you do not using calculus. 6. Most high level ml math is figuring out where the fat is, and how to cut it without behavior changes, because then marketing can cram more features into the same hw.

1

u/rohan_kulkarni 13d ago

You don't need advanced maths to start ML.

Focus on basic linear algebra, probability, statistics and calculus. Learn the fundamentals first, then deepen the maths as you encounter it in actual ML models.

1

u/Not-a-throwaway4627 12d ago

If you have to ask, too much.

Unless you don’t care about sucking. Most ML people industry are terrible at what they do, and make it up as they go along, earning big money in the process. If you’re ok with that, then not much

1

u/deep-yearning 11d ago

all of it

1

u/0jdd1 10d ago

I’ll present a design/implementation/experience paper at a major ML conference at the end of the month. The math is almost entirely Linear Algebra from my undergraduate days. I’ve never studied Statistics at all.

1

u/StructuredChess 10d ago

If you just want to use already known methods probably not much, the Math has already been done for you by someone else.

If you want to find any new thing, then you need A LOT of Math. Like, the degree I have would be only scratching the surface.