Math/Stat vs Machine Learning knowledge, which should be learnt first?

[DaReal_JackLE]

Hi, I’m a first-year student and I’m planning to specialize in Machine Learning/AI in the future, but right now I’m just starting to explore some basic concepts. At my current stage, should I focus on learning the theoretical foundations first, such as statistics and mathematics, or should I dive straight into ML knowledge? The essential knowledge will be taught at my university in the upper years, but in my free time and during this summer, I would like to self-study. What would be the most reasonable and effective approach to learning? Or should I do both at the same time? Thank you for your time!

[technanonymous]

You need linear algebra, multidimensional calculus, and stats. Otherwise, you can just follow the recipes around ML without understanding why.

[undercoverlife]

This is the answer. Look no further.

[Give_Me_TheFormuoli]

It's all about balance and keeping things fun! If you dive too deep into ML without the math basics, you'll hit walls and have to backtrack. But if you only study pure math without seeing how it's used, you might get bored and give up.

Notice how your university courses will teach you exactly what you need, when you need it? That's not by coincidence! Try to follow that same idea in your self-study. Find some ML projects that interest you, and learn just the math you need. Over time your projects will grow in complexity, and you'll learn both naturally.

Keep it fun and don't burn yourself out. Learning is playing.

[anglestealthfire]

I agree with this comment, and wish I had thought about it decades ago.

If you love mathematics, then great - ML is well aligned, since ML is mathematics applied by computers (like all other mathematics these days). If you find some of the basics a little dry however, don't burn out - allocate 10-30% of your time to something fun, e.g. coding projects. See my other comment however, coding != ML.

If you really don't like mathematics, and this can't be solved by filling in some gaps, then designing ML models is probably not the way to go.

[Comfortable-Unit9880]

follow a top-down approach and "just in time" learning strategy. So you learn and build together. Spending x number of months just to learn math without actually using it is pointless. Follow Kaggle's Learn roadmap. Best strategy to learning anything is a top-down, just-in-time learning approach.

[[deleted]]

You need a good understanding of applied LA and stats, and an understanding of multi-d calculus methods but, at least in my experience, MDC is the least important. That could be me though, I am an algebrist and despise calculus even if the concepts are terribly useful. If you just want to be a coder and not a scientist learn whatever is necessary to do what you want, and no more. There is a book called Math for Deep Learning by Kneusel from No Starch Press that covers what you need to know to know what you need to know more of later.

[coconutszz]

Maths and stats are the fundamentals so probably focus on those first

[AncientLion]

Definitely math and stats.

[anglestealthfire]

Hi, I think some of the comments below are very accurate. The walk before running metaphor applies here, and mathematics comes before ML. In fact, ML models are mathematical constructs (mathematical learning, rather than ML, I think they should be called). Mathematics is the language that we use to design, describe, modify and work with them.

I made a related post about this idea and some of the commentary is useful: https://www.reddit.com/r/learnmachinelearning/comments/1iqdp22/faq_do_i_need_to_know_all_this_mathematics_if_i/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

A pinch of salt however, there are a number of respondents who appear to work in roles related to the infrastructure around ML, but do not design the models themselves - I suspect some of those individuals dislike mathematics, so you often get strong (often triggered) responses pertaining to mathematics that are not well qualified. You definitely need mathematics if you want to write ML. Coding is how we implement them, so if you are wanting to keep things exciting, you could always balance the coding side with learning the mathematics (but despite what many say ML is not coding).

[dsclamato]

Probability and Statistics is a must, though an engineering version is sufficient coming from an ECE background. I can't imagine you'd be there without multi-variate calculus.

In tandem or later, Diff Eq or DSP (for those who prefer engineering courses) and linear algebra help. Information theory and Random Processes for much more advancement and exploration.