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Comment by jokoon | Hacker News Reader

Yeah I have nothing against algebra or math, I like math, and I know some algebra, but he was constantly using mathematical notation instead of algorithms. It was not accessible.

I got into many arguments about how computer science is math and so on, but machine learning is a set of techniques, there is no need for it to use math when it's not really needed.

So I gave up. I did not even manage to properly understand his explanation of linear regression, as I would have liked to implement my own. When I look online it's a myriad of equations, and no pseudo code.

I love mathematics, but when I'm programming I really prefer to use data, and not math.

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While that approach might help you understand how to code a technique, it doesn't do much to teach you to evaluate when and why to use a technique. You can read a bunch of tips on when to use something, but simply remembering these "facts" rather than being able to relate them from first principles is not efficient.

Granted after you understand the math, you no longer need an equation, but simply a mental picture of what is going on (for example, I remember linear regression with projection of data onto a line or plane).

After you have a good foundation, I found often-times you can then ignore the details of equations and see the relevant parts (different forms of Bayes rule, projections, chain-rule, etc) and arrive at the general idea of a technique.

The interesting and hard part is understanding the technique, and I think that is probably the most bang for your bucks, as I assume you are proficient in programming already.

ML is a set of techniques derived from math.

Instead of giving up you should push through that. Use what you know to get familiar with what you don't, rather than asking for things to be made accessible to you. Math is much older and bigger than computer science and refusing to learn it is over-specialization.

Well if it's derived from math, give the techniques, not the math.

I don't care if it's accessible for me or not, it seems the guy is saying ML is the new electricity, lowering the bar won't only benefit me, but many other people who know programming and don't benefit from knowing the math.

Just my opinion. Many people and hackers learn by practice, not by theory.

We need both electrical engineers and licensed electricians. If you want to be an electrician, don't sign up for an engineering class taught by a pioneer in the field of electricity and then complain that there is too much theory. Coding bootcamps and vocational schools already exist to meet the needs of those who only want to be practitioners.

Everyone learns by practice, but that applies to math just as well as programming. If you don't practice it, you won't learn it.

> Everyone learns by practice, but that applies to math just as well as programming. If you don't practice it, you won't learn it.

I don't see machine learning as a theoretical field of math, I see machine learning as a set of tools.

To me computers are the tool you use to practice math, and you don't really need to use mathematical notation anymore, unless you really dive into a very abstract subject. ML consists of applied methods. I don't think people want to learn the theory of machine learning, they just want to know the methods that works now, and maybe dive into more complex stuff later.

I was actually disappointed that there was not enough mathematical discourse in the course. He would mostly present you a formula without going much into details on how it was derived. It does make sense though that he dumbed down mathematical bits to fit a more applied purpose of the course.

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