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tldr; The experts make it look impossible but the masters make it look easy and actually teach you Just ranting out here so feel free to ignore :-) This is no

tldr; The experts make it look impossible but the masters make it look easy and actually teach you Just ranting out here so feel free to ignore :-) This is not really aimed at the author but towards the "elite" group. There was another elite commentator in one of the other thread who said he dropped out of ML class because This course included gems such as "if you don't know what a derivative is, that is fine" and he thought math was important in ML. Before the ML class I could not even argue with these guys because I did not know squat about AI and talking to these experts their advice was to take a year off and learn math and then start learning AI which in my case was not possible. Today after a couple of months of online classes I am actually using ML in my daily work and its not magic that only the elite with deep profound math knowledge can use. Another programmer who is working in khan academy actually had a blog post about how he implemented ML by learning from Prof Andrew's class now that is real world impact. I may be missing something but can one of you experts please explain why you need deep math knowledge when the professor who has been doing a lot of research in this field a lot more than you does not think so ?. The professor in his classes keeps reassuring that even after using it for so many years he has difficulty in the subject but I'm guessing these experts know it all :-). This is the reason in my opinion even though wall street is full of smart people they do not care about the rest of the population or the general masses the attitude is we are smart and we can do what we want you guys are dumb and deserve what you get and if someone outside of their elite group starts talking their language they do not like it. On a similar note when you look at the people complaining about khan academy most of them are these so called smart people. Let me talk about my background I have been working as a programmer for around 11 years , no math background though thought myself math by using Khan academy and before my layoff (now am working on my own startup ) used to make 90K (in a southern state). So guys you are not the center of the world we are crashing into your fraternity you are no longer the only experts who can talk about ML , the guys at stanford are smarter than you and know what they are doing and FYI they don't need you its the other way around. Another interesting thing is that mostly the current students seem to agree with the author, If you are smart you should probably take the effort to learn more rather than asking them to tailor the classes to what you think matters . Also ask yourself this question if you were the Professor what do you think is more satisfying teaching 40 full time students or 20000 who are in the field already and make more impact in the field ?.

sausagefeet 15y ago
This could just be an expectation problem. I think most of these free courses have that 'Applied' stuck infront of them, ok, fine. But for my education I want rigor. I don't want to just know how to use something, I want to know how the guy who came up with it figured it out and I want to be bale to prove things about it. Not having to know what a derivative is does not fit this. I don't think it's a matter of the elite thinking only elite people can grok something like ML, it's a matter of that they expect the dirty details and are annoyed when they don't get it.
- noblethrasher 15y ago
  I know ML and have a math degree; I don't see why you'd need to know what a derivative is. If anything, I think exposure to ML and functional programming before differential calculus could be beneficial since you'd better appreciate that differentiation is just one special application (no pun intended) of the concept of higher order functions.
  - flatline 15y ago
    If I recall, solving back propagation in multiple-layer perceptrons was an unsolved problem for some time, and the solution relies pretty much solely on partial differentiation. I don't know much about ML but things like neural networks were pure mathematical constructs before they were CS topics. I agree with the GP, though, you don't need to know the actual math for most of this stuff.
    - disgruntledphd2 15y ago
      On the point regarding the necessary knowledge of maths for ML (or indeed statistics which is the same material but a slightly different focus), I'm conflicted. Coming at it from my perspective (learned a lot of math in high school, forgot most of it until I started a PhD), i would agree that a lot of the time, you don't need to understand the mathematical underpinnings of this stuff. That being said, as I've learned and remembered more of the math, my capability to understand (and debug errors) of all of this has increased tremendously. I do think, if you intend to use ML every day, then you need to commit to understanding everything you use within a certain time frame of you beginning to use it (ideally immediately but that's often not possible). Anyway, derivatives are cool, and transform the way you look at the world, so you should definitely learn some of those.

sausagefeet 15y ago

This could just be an expectation problem. I think most of these free courses have that 'Applied' stuck infront of them, ok, fine. But for my education I want rigor. I don't want to just know how to use something, I want to know how the guy who came up with it figured it out and I want to be bale to prove things about it. Not having to know what a derivative is does not fit this. I don't think it's a matter of the elite thinking only elite people can grok something like ML, it's a matter of that they expect the dirty details and are annoyed when they don't get it.

noblethrasher 15y ago
I know ML and have a math degree; I don't see why you'd need to know what a derivative is. If anything, I think exposure to ML and functional programming before differential calculus could be beneficial since you'd better appreciate that differentiation is just one special application (no pun intended) of the concept of higher order functions.
- flatline 15y ago
  If I recall, solving back propagation in multiple-layer perceptrons was an unsolved problem for some time, and the solution relies pretty much solely on partial differentiation. I don't know much about ML but things like neural networks were pure mathematical constructs before they were CS topics. I agree with the GP, though, you don't need to know the actual math for most of this stuff.
  - disgruntledphd2 15y ago
    On the point regarding the necessary knowledge of maths for ML (or indeed statistics which is the same material but a slightly different focus), I'm conflicted. Coming at it from my perspective (learned a lot of math in high school, forgot most of it until I started a PhD), i would agree that a lot of the time, you don't need to understand the mathematical underpinnings of this stuff. That being said, as I've learned and remembered more of the math, my capability to understand (and debug errors) of all of this has increased tremendously. I do think, if you intend to use ML every day, then you need to commit to understanding everything you use within a certain time frame of you beginning to use it (ideally immediately but that's often not possible). Anyway, derivatives are cool, and transform the way you look at the world, so you should definitely learn some of those.