Submitted by tylerferreiraa t3_ymu2ml in MachineLearning
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Submitted by tylerferreiraa t3_ymu2ml in MachineLearning
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Great advice, thank you
Ideally, you need both. But If you have to choose, then I’d recommend statistics. Just make sure you understand derivatives, gradients and integrals.
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.
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.
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You need to understand statistics, probability, and multivariate calculus. You can learn all of that without a college course. So pick the courses that you think you would need the most help to understand. Something you would find difficult to learn on your own.
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.
Yes hahaha
Yes, all would be good. I’d also add Linear Algebra to the list. Differential Equations would be the least beneficial.
Yes, I have to do discrete maths and linear algebra as they’re requirements then 2 math electives from the following above :)
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.
UncertainLangur t1_iv5h6iq wrote
You need a rigorous course in multivariate calculus, and mathematical statistics. Pepper it with CS courses whenever possible.