Submitted by tylerferreiraa t3_ymu2ml in MachineLearning
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Submitted by tylerferreiraa t3_ymu2ml in MachineLearning
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Ideally, you need both. But If you have to choose, then I’d recommend statistics. Just make sure you understand derivatives, gradients and integrals.
Yes, all would be good. I’d also add Linear Algebra to the list. Differential Equations would be the least beneficial.
You need a rigorous course in multivariate calculus, and mathematical statistics. Pepper it with CS courses whenever possible.
Thanks for the response - So would you say calc 2 and stats 2? Or perhaps Stats 2 and probability instead?
I’ve currently done calc, relational algebra(in a database class) and i’ll be doing linear algebra and discrete maths also as they’re requirements.
Yes, I have to do discrete maths and linear algebra as they’re requirements then 2 math electives from the following above :)
Whichever courses seem higher quality in your specific instance. Like is the professor well-regarded? Is the coursework rigorous and relevant? What do people say about the class?
If you've never done calculus, it seems prerequisite for deeper probability/stats, so I'd lean slightly that way.
Great advice, thank you
I could not possibly advise you on this. I don’t know the contents of the courses. Generally speaking; if you know what a Gaussian distribution is, how to calculate gradients, integrate a function and multiply matrices - then you’re off to a good start.
For applied ML, stats comes more handy more frequently, and you can gloss over the calculus, but for theoretical ML or research in ML methods, or having a good grasp of how these algorithms work, you’ll need both. And advanced mathematical stats also depends on calc BTW.
mkzoucha t1_iv5gsd9 wrote
Yes hahaha