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Ask HN: Learn Math the Hard Way

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Re: Ask HN: Learn Math the Hard Way

#71

Just follow the usual path for education in relatively applied math. Here is a nutshell description: The standard high school level subjects are algebra, plane geometry, second year algebra, trigonometry, and solid geometry. The standard college level subjects are calculus, abstract algebra, linear algebra, advanced calculus, ordinary differential equations. Might also take elementary courses in probability and stati…

I am intending on doing distance learning, with non-physical contact with lecturers / fellow students. Do you think it's possible yo achieve mastery this way? Are there online communities etc. which could help?

Re: Ask HN: Learn Math the Hard Way

#72
post #52
post #38

Earlier quoted context omitted.

You say 'trig comes up a surprising amount'. It would be great if you could give a few examples of these instances. I have always thought of trigonometry as one of the areas I really enjoyed in school but something I have never used ever since.

I think the surprising commonality is conditional. So you won't see it unless you are focusing on physics or signal processing.

Trig? Fourier series and its introduction to functional analysis and Hilbert space and the ideas of inner products and orthogonality there. JPG image compression. Shannon's information theory. Characterization of time invariant linear systems, e.g., acoustics. Antenna theory. Phased arrays and beam forming. Imaging in optics. Holography. The fast Fourier transform. Seismic data analysis via the fast Fourier transform. Characteristic functions and Bochner's theorem. Power spectra. X-ray crystallography. The fundamental theorem of interpolation and the Nyquist sampling theorem (e.g., how music CDs work).

There's a cute idea to put 100 wireless customers all on the same wavelength. Basically have a tricky antenna pattern with a lobe for each user and so that each user gets only their own signal. It's all trig.

Re: Ask HN: Learn Math the Hard Way

#73
I recommend Susanna Epp's Discrete Mathematics with Applications, which I recently taught a university course out of quite successfully. It offers a gentle but rigorous introduction to proofs, logic, and combinatorics, with applications to CS. Useful exercises too.

It is squarely targeted at beginners, if you know the stuff already then many other commenters have recommended more advanced books.

Re: Ask HN: Learn Math the Hard Way

#74
post #62

Earlier quoted context omitted.

Meh. If you want to learn hard math to make it harder, and to give you background for stuff you might encounter, why not go whole hog? Go ahead, learn algebraic topology so that you can understand category theory properly, then when you encounter it in CS you'll know what people are talking about. I think there is a point of diminishing returns. Baby Rudin I'm dubious about. But Royden and big Rudin (both of which yo…

> I think there is a point of diminishing returns. Yes, there is a big question about what to learn, about how much to invest in such things. > Baby Rudin I'm dubious about. But Royden and big Rudin (both of which you recommended) I have certainty about. But Baby Rudin is a prerequisite to Royden and big Rudin. I'm sorry, but probability, stochastic processes, and mathematical statistics were junk for me until I went…

OK, what it sounds like to me is that you found a CS niche that needs a ton of measure theory.

That does not tell me that this will be the future of CS. But there are niches where this stuff is more applicable than I had realized.

Re: Ask HN: Learn Math the Hard Way

#75
post #67
post #66

Earlier quoted context omitted.

You argument is solid and it would be great if math was taught more as a connected whole but many do not have that luxury. Key though, is the author never went into much detail as to his or her intentions and motivations so not much can be said if your list is inspiring or too intimidating. One thing I'd like to point out is that measure theory is not the only and probably the least interesting way to study probabili…

Nonstandard Analysis is an interesting side note. But pulling out the axiom of choice to differentiate x^2 is a bit much IMO. (Yes, I'm aware that there are different ways to construct the nonstandard model. But the subtleties needed to really understanding NSA are substantial. I far prefer the little-o approach that Knuth recommends.)

Perhaps you are more familiar with Robinson's derivation but Keisler has an axiomatic approach that makes calculus easier for students than regular calculus (besides by the time you are doing continuous differentiation you are already calling on some far out concepts like real numbers and inifinity).

And real understanding takes time no matter what you do, the best one can do is start off in a manner such that the tools required to understand well enough are not more complicated than the subject matter itself.

Re: Ask HN: Learn Math the Hard Way

#76
post #74

Earlier quoted context omitted.

> I think there is a point of diminishing returns. Yes, there is a big question about what to learn, about how much to invest in such things. > Baby Rudin I'm dubious about. But Royden and big Rudin (both of which you recommended) I have certainty about. But Baby Rudin is a prerequisite to Royden and big Rudin. I'm sorry, but probability, stochastic processes, and mathematical statistics were junk for me until I went…

OK, what it sounds like to me is that you found a CS niche that needs a ton of measure theory. That does not tell me that this will be the future of CS. But there are niches where this stuff is more applicable than I had realized.

The future of CS will be in several, maybe many, directions. E.g., there will be new struggles keeping the cores busy when we have 1000 cores per processor. My software development is taking too darned long; one direction is to make that faster.

But one direction is handling 'randomness', and for that I recommend a measure theory foundation of probability, stochastic processes, and mathematical statistics. Just a recommendation. It's risky; your mileage may vary. As Yoda said, "Always difficult to see, the future".

Re: Ask HN: Learn Math the Hard Way

#77
post #66

Earlier quoted context omitted.

You argument is solid and it would be great if math was taught more as a connected whole but many do not have that luxury. Key though, is the author never went into much detail as to his or her intentions and motivations so not much can be said if your list is inspiring or too intimidating. One thing I'd like to point out is that measure theory is not the only and probably the least interesting way to study probabili…

Do you have any book recommendations for a nonstandard analysis treatment of probability? I'm really only familiar with the measure theory approach, myself. (In fact, I've been known to say that probability is the study of measurable functions with finite, nonzero integral over the real line.)

The standard is Nelson's Radically elementary probability.

Vovks and Shafer's game-theoretic approach is interesting in that related approaches like bandit models and online learning have recently picked up in popularity.

Re: Ask HN: Learn Math the Hard Way

#78
post #52

Earlier quoted context omitted.

I think the surprising commonality is conditional. So you won't see it unless you are focusing on physics or signal processing.

Trig? Fourier series and its introduction to functional analysis and Hilbert space and the ideas of inner products and orthogonality there. JPG image compression. Shannon's information theory. Characterization of time invariant linear systems, e.g., acoustics. Antenna theory. Phased arrays and beam forming. Imaging in optics. Holography. The fast Fourier transform. Seismic data analysis via the fast Fourier transform…

Indeed, I agree. Those all fall under the umbrella I gave. But those are all particular subject areas that many people can go through their career without touching.

Re: Ask HN: Learn Math the Hard Way

#79
post #71

Just follow the usual path for education in relatively applied math. Here is a nutshell description: The standard high school level subjects are algebra, plane geometry, second year algebra, trigonometry, and solid geometry. The standard college level subjects are calculus, abstract algebra, linear algebra, advanced calculus, ordinary differential equations. Might also take elementary courses in probability and stati…

I am intending on doing distance learning, with non-physical contact with lecturers / fellow students. Do you think it's possible yo achieve mastery this way? Are there online communities etc. which could help?

The first-cut, rough answer is, yes, you can. Another answer is, no matter what, most of what you have to do has to be done alone in a quiet room with the book, or books, lecture notes, or whatever.

But there are some risks.

First, broadly a big risk is getting off track. There are many poor books, and dig into one of those and might not ever come out. And there are side streets; might go down one of those and never get back to the main road. Don't do those things.

So, the lessons: Pick subjects carefully. In any subject, use only very highly recommended books, and use more than one in parallel although likely one book as your 'primary' source and the others as 'supplementary'.

For book recommendations, get those from a better source than Hacker News! I suggest getting recommendations from some of the best courses and professors in the departments of at some of the world's best, and I mean at top 20 or top 10, research universities. So, for a subject you want to learn, find what books such courses, profs, departments, and universities are using and recommending.

Second, another big risk is getting stuck. One way is to encounter an exercise you can't solve, believing that you are missing something important, and then just grinding to a stop, for days, weeks, or forever, on that exercise. Don't do that.

(A) There are a lot of 'misplaced' exercises. Just because an exercise is in a book doesn't mean that it is reasonable to be able to solve it yet. Yes, in a good book you should be able to solve 90% of the exercises, but one book may have 1-20 exercises that are just nasty chuckholes in the road. I know one beautiful book where the exercises are so difficult, and apparently usually beyond the book, that one should get a prize for each doable exercise they find! (B) There are some tricky exercises that are just tricky and not really important. (C) There's no way you are going to miss much if you skip a few exercises. (D) You can continue on, maybe ask for help, look at your supplementary sources, etc. and then either get a solution or conclude that you haven't missed much. Yes, do work hard, but definitely don't get stuck. After enough hours at one spot, just move on.

Going on will sometimes give you the tools you need to resolve the place you were having trouble. Generally it's no sin, and sometimes helpful, to rush ahead quickly, get an overview, and then return to a more complete pass, and the overview can help you judge if the place you were having trouble was important or not. Also there's no way just one pass over some material can be as effective as three or so. E.g., one goal is a 'synthesis' of the material, and that needs several passes and several sources.

Third, there are occasional actual errors in the sources. So, if something doesn't make much sense, then it's wise to suspect an actual error someplace. You might be able to resolve the issue by consulting supplementary sources, doing some derivations on your own, say, to find a counterexample, or asking for help. By now the Internet may actually be a good place to ask for help. And, there's no law against writing the author of a book!

Fourth, some books are just much better than others. And even a good book might make a mess out of a few topics. So, again, consider several sources.

Fifth, there is a broad point about 'standards of quality'. Math is by a wide margin the most precise of all academic subjects. Still, in the total body of math that is done currently or already on the shelves of the libraries, the standards of quality vary widely.

Some of the most highly polished math ever put on paper is by (the collection of largely anonymous mathematicians) Bourbaki; very little of the rest of math is so highly polished; some of the best math and math writing is not as highly polished as Bourbaki.

So, there is a danger: It's possible to work to standards that are, in some or nearly all respects, too high. Beware that in the best work, the best of that work is not some high polish but in the real 'content' that would still be good just copied off a blackboard.

Why high polish? Because it can make one more immune to criticism. But, even Bourbaki gets criticized. Some criticism will always be there. So, don't try to defend against all possible criticism.

So, the 'standards of quality' that might be worth emphasizing would be the real 'content', the 'meaning', the 'significance', the 'power', the 'meat' and not secondary issues such as polish.

Contact with a good university department will give you some insight into what standards of quality you want to pursue, not too high, not too low, worthwhile and not just wasted effort, etc.

As I've written in this thread, it may be important for you to have a good teacher for abstract algebra since that is where your approach to math will take a big change to how the rest of math works -- definitions, theorems, and proofs. You will need to learn how to get the understanding, intuition, 'conceptual models', helpful pictures, promising applications, the meaning, the 'meat', etc. out of presentations that look just abstract or like just abstract nonsense; of course, attempt to do such things only for what are without doubt high quality presentations.

You will need to learn how to write math with definitions, theorems, and proofs, and for that you may need a good prof to read and mark your homework.

If you tackle abstract algebra without a teacher, then beware of these 'side' lessons you need somehow to learn and make some effort, likely not very large, to teach them to yourself. In particular, look at some beautifully written math, e.g., John von Neumann, e.g., his 'Quantum Mechanics', Paul Halmos, Leo Breiman, and pay close attention how they write.

All the warnings aside, if you are careful about avoiding the dangers, then you should be able to do well.

Let me suggest, generally some occasional contact with a good university department should help. E.g., if you work through Halmos 'Finite Dimensional Vector Spaces', Rudin's 'Principles of Mathematical Analysis', MacLane and Birkhoff's 'Algebra', or Leo Breiman's 'Probability', along with supplementary sources for each, then maybe ask to sit in, totally informally, just to observe, one or a few sessions of a corresponding university course. And get some homework assignments and some test copies. Then see if it appears you understood the material. You might be pleasantly surprised! Also, get old copies of the qualifying exams -- if these are easy for you, then you are doing well!

At least at one time, the Princeton math department, a good candidate for the best in the world, just stated that the graduate courses were introductions to research by experts, that no courses were given for preparation for the qualifying exams, and that students were expected to do such preparation on their own. Well, Princeton was stating that they expected students to do much like what you are trying to do, independent study.

There is a big theme: It's a good guess that what's valuable in math is creating new stuff. For a Ph.D. or tenure at a research university, research is the main work. For valuable applications to problems outside math, it may be that work that is at least a little new will be much more valuable.

Then, nearly always original work in math is done largely or totally independently. So, net, at some point, being good at independent work is crucial.

Re: Ask HN: Learn Math the Hard Way

#80
post #75
post #67

Earlier quoted context omitted.

Nonstandard Analysis is an interesting side note. But pulling out the axiom of choice to differentiate x^2 is a bit much IMO. (Yes, I'm aware that there are different ways to construct the nonstandard model. But the subtleties needed to really understanding NSA are substantial. I far prefer the little-o approach that Knuth recommends.)

Perhaps you are more familiar with Robinson's derivation but Keisler has an axiomatic approach that makes calculus easier for students than regular calculus (besides by the time you are doing continuous differentiation you are already calling on some far out concepts like real numbers and inifinity). And real understanding takes time no matter what you do, the best one can do is start off in a manner such that the to…

Yes, pedagogically you can just use infinitesmals after an appropriate hand wave. Most students will ignore the complicated details and just absorb the infinitesmal picture. If you take the attitude that students just need to learn the formulas, then it doesn't matter what approach you take.

However you've created difficulties for the day that they dive back in and try to really learn the subject. Because as they go to learn what a real number really is, and what its properties are, and about pathological functions, they also have to learn about the hyper-reals and a complex model-theory construction that (in both variations that I am aware of) requires choice.

There are a variety of other pedagogical choices that do not present such barriers to comprehension. (Note, by no means am I a fan of the limit approach. I know full well that it goes over the heads of the students, and I see no point in having the person at the front blather on about stuff that the class is not able to expected to understand.)

In any case my biggest complaint about how Calculus is taught is this. I think people come out of a first Calculus course without understanding the tangent line properly. If you don't understand the tangent line, Calculus is a mass of formulas. Despite how easy it is symbolically to jump directly from the tangent line to the derivative, I think that a solid week should be spent on the tangent line (calculating it for more complicated functions, finding applications, etc) until it is every student understands it well enough that they are ready to take the leap of looking at the slope and building a function out of it.

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