* Geometry and the imagination by Hilbert and Cohn-Vossen
* Methods of mathematical physics by Courant and Hilbert
* A comprehensive introduction to differential geometry by Spivak (and its little brothers Calculus and Calculus on manifolds)
* Fourier Analysis by Körner
* Arnold's books on ODE, PDE and mathematical physics are breathtakingly beautiful.
* The shape of space by Weeks
* Solid Shape by Koenderink
* Analyse fonctionnelle by Brézis
* Tristan Needhams "visual" books about complex analysis and differential forms
* Information theory, inference, and learning algorithms by MacKay (great book about probability, plus you can download the .tex source and read the funny comments of the author)
And finally, a very old website which is full of mathematical jewels with an incredibly fresh and clear treatment: https://mathpages.com/ ...I'm in love with the tone of these articles, serious and playful at the same time.
I've had this idea of starting back at basics and relearning math from the beginning since I never "really" learned it besides memorizing and skirting my way through it in school.
Do you know a good path or book that's suitable for that?
I bought myself a Remarkable 2 and signed up to Khan Academy. Now I'm revising algebra basics and I plan to go as advanced as Khan Academy lets me.
I was really bad at maths in school (UK A Levels). But I'm a successful software developer today. I felt like knowing more advanced maths could make me a better developer and not feel intimidated by a lot of the things I see.
I'm actually enjoying it as well. Maths isn't just something I have to do to get out of school, now it's something I want to do. And it gives me the same satisfaction as solving puzzles like sudoku.
I'd recommend it to anyone. The Remarkable 2 is actually really nice to write on too, since I want to store my notes digitally. And I make so many mistakes when writing, so undo is great.
Thinking of doing the same thing. The last math class I had was at 16, and the most advanced classes was on binary, so not very complex stuff.
I've mostly been winging it for another 16 years and seemingly picked up things here and there. But math is definitely been trial and error, and I definitely do not know the lingo in math, which I'm now starting to feel is a big disadvantage.
also: the remarkable 2 is great, we have one, but the screen broke and the refurbished replacement arrived with a screen that's not functioning correctly at all, making it a unusable device. Good reminder to reach out to them again.
If you don't mind the question how is the Remarkable helping you in this. Just to avoid the clutter of paper? Or does it some how OCR you're handwriting?
I hate the clutter of paper and how hard to organise it is for me. Plus, how messy I am writing on paper and crossing things out all the time.
The Remarkable, at least for me, is good because I can organise my notebooks into folders by certain math lessons or concepts. And I can undo any mistakes, so my notes are clean. Even if I am quickly working something out I am clean it up and make it a good note for future me. The feel of writing on it is much nicer as well versus my laptop's pen or my iPad's pen.
Also, I like that it basically just does notes. There's no Android bullshit, it's just no nonsense note taking. Some competitor tablets have Android and all that baggage.
It can OCR you writing, but I don't know how good that would be for math.
The Remarkable isn't the only tablet that can do this, but it's the one I bought because I like the style and the simplicity of the software.
A similar solution might be https://getrocketbook.com/ RocketBook. It can be a $0 dollar solution if you download their app and then print out the free PDF pages that are pre-formatted.
I have a remarkable 2 and a Microsoft Surface Pro, intended to de-clutter math coursework. Both work, but I found that the real estate on the remarkable was too limited, even though its a great device, so I tried the Surface Pro. I can fit just about any sized work onto it, and you can endlessly scroll down which was something I couldn't figure out on the remarkable. It makes doing math easy or at least takes away some housekeeping which I find really distracting. And saving and organizing work and being able to import and export files is a bonus.
Sorry, out of topic. May I know what's your definition of success?
I've been doing software development for 10 years but success seems to be always on the horizon.
The reason I find them fascinating is that Wildberger doesn't agree with some of the conventional approaches, in particular with the use of infinity and taking limits. This leads him down interesting paths (e.g. Rational Trigonometry and Algebraic Calculus), which (a) show the process of mathematics (exploring, making definitions, building up in different directions, etc.), whilst (b) remaining mostly grounded and approachable (e.g. no appeals to inscrutable lemmas from abstract research areas).
For example, he's recently been making videos about "multisets" (computer scientists would call them Bags), their arithmetic (where "adding" is union, and "multiplying" is pairwise/cartesian product of the elements), and how this generalises: from an algebra containing only empty bags (trivial, but self-consistent; behaves like zero), to bags of zeros (behaves like natural number arithmetic), to bags of natural numbers (behaves like polynomial arithmetic), to bags of polynomials (behaves like polynomials in arbitrarily-many variables) https://www.youtube.com/watch?v=4xoF2SRp194
"The reason I find them fascinating is that Wildberger doesn't agree with some of the conventional approaches, in particular with the use of infinity and taking limits."
So no transfinite ordinal analysis or large cardinals? Hard to take him seriously.
> It's the hypotenuse of a right triangle with legs of length one.
Yes, we can construct such a line segment; but line segments are not numbers.
We don't actually need "legs of length one" (which pre-supposes some system of units); all we need is the ratio of the lengths of the sides. However, finding lengths requires the ability to take square roots, which would either make this a circular definition (e.g. that √2 = √2 / 1), or requires the limit of an infinite process (like Newton's method, or equivalent).
Instead, it's much easier to count the areas of the squares on each leg (1 and 1), and add them together to get the area of the square on the hypotenuse (1 + 1 = 2). No need for lengths, so no need for square roots, so no need for √2.
Wildberger abbreviates 'area of the square on a segment/vector' as the 'quadrance' of that segment/vector (defined as the dot-product with itself). Likewise we can avoid angles by taking ratios of quadrances (e.g. 'spread' is defined via a right-triangle as the quadrance of the opposite side / quadrance of the hypotenuse); together this gives rise to a whole theory of Rational Trigonometry, which gives efficiently computable, exact answers; works in arbitrary fields (except for characteristic two), and with arbitrary dot-products/bilinear-forms (e.g. euclidean, relativistic, spherical, etc.). Here's Wildberger's textbook on the subject http://www.ms.lt/derlius/WildbergerDivineProportions.pdf
no computer can calculate that exact distance, which is kind of Wildberger's point.
Infinities are very interesting but the non-infinite maths have kind of got neglected over the past 100 years. I had to memorize Laplace transforms in college but never heard of Fairey sequences until I watched his videos.
People get upset at him but he's basically just having fun seeing how far you can go in Math without infinity. It's quite interesting to a certain audience (like myself).
I’m reading and like Thomas Garrity’s “All the mathematics you missed (but need for graduate school)” which is this but for people who did a bachelors degree but missed certain areas (or forgot them).
Something else I’ve found extremely useful in getting into maths topics is the Princeton Companion to Mathematics - it doesn’t have exercises but gives excellent overview essays of a wide range of maths topics - expensive to buy (mine was a present) but should be available in academic libraries, say.
I bought and read it (more like, skimmed) and liked it a lot too.
Gives a bird's eye view of math very nicely. Even from a skimming it was very useful to help me understand the gaps I have, and the shape of those gaps, and partially filling them.
3Blue1Brown videos seem like a good resource to use along any book. My experience as a math major (in the distant past) is that the kind of visualization the author shows you is also something you want to imitate in your head when you are learning new concepts. I find things I learned in this level tended to stick in my head 10+ years later, other stuff less.
I should mention focusing on doing a few interesting problems, rather than many not so interesting ones, is also one way to help yourself understand more deeply.
Lots of easy problems is a good way to build up muscle memory, though. IMO the brute-force method of, say, Saxon Math really makes sense for things like basic elementary school algebra and probably intro calculus, where the student is sort of learning the math equivalent of how to walk. Not sure where the switch over ought to be, though.
we tried "Saxon math", Singapore math dimensions, and Beast Academy.
And my impression was that Saxon Math was the worst.
What I mean by worst is that it just make you practice an algorithm by doing lot of repetition but doesn't force you to have a deep understanding or problem solving skill.
Saxon math worked out for me, although we didn’t shop around as far as I remember, so maybe Singapore would have worked fine as well.
My experience is that I didn’t really feel like I was memorizing an algorithm. Because the problem set includes assignments from all of the old sets, it is hard to memorize all of the algorithms. So you instead memorize the different moves that are allowed and have a general idea of what types of moves might be useful.
I dunno. I went on to do engineery stuff as an undergrad rather than pure math stuff, it seems like a good match because engineering problems are also often in the “no need to be super clever, just don’t mess up” vein, so it could be just a lucky match. This is what I mean by muscle memory — I’ll use the famous names theorems when necessary but sometimes you just need to bash the math until the thing you want is on that side of the equal sign and the other stuff is on the other side.
I think anything that results in
1) actually reading some textbook
2) actually working through problems for a couple hours a week
will compare well to the typical US math education pretty well anyway.
I'm doing this and am starting with Linear Algebra on MIT OCW (taught by Gilbert Strang). My current plan is to relearn Linear Algebra, Calculus, Probability, and Statistics and actually focus on retaining the knowledge in my memory using something like SRS learning. I think planning past that is pointless since by the time I'm done I will have a better ability to plan my future coursework.
I did this same exact thing back in 2010. I used khan academy for it. Started with positive and negative numbers, arithmetic, through trig and algebra.
I like khan academy back in 2010 because all the videos were in one place and you could see everything right there in front of you
I had a similar thought back in 2014. I had only studied the maths required for various engineering courses I’d taken.
So, I decided I wanted to study maths for the maths.
I was in the fortunate position of being able to self fund myself through the Open University (uk based) Maths and Statistics BSc.
One module at a time I’m now on my last module.
There many things I’d studied before (calculus, sequences) and many new to me (group theory, graph theory)
I’ve been doing a similar thing with Brilliant and really enjoying it. It feels like every course is orientated around teaching maths from a problem solving perspective so you actually get why you’re learning stuff rather than teachers just trying to brute force things into your head which unfortunately seems to be the default at schools nowadays.
Do you know a good path or book that's suitable for that?
I've been using Professor Leonard's Youtube video series[1] mostly, along with some of those "workbook" type books by Chris McMullen, and a variety of books with titles like "1001 solved problems in $SUBJECT", "The Humongous Book of $SUBJECT problems", and the like. The nice thing about Professor Leonard is that he has videos on everything starting from pre-algebra, middle-school math, up through Differential Equations. Note that his diff-eq class isn't quite complete but he just announced he's about to start recording new videos to finish that, and he's also going to be starting a Linear Algebra sequence. And he's a great lecturer who does a really good job of explaining things and making them understandable.
I also use Khan Academy sometimes, and stuff on Youtube from The Math Sorcerer[2]. Oh, and of course there is 3blue1brown[3], whose videos are also useful. And for Linear Algebra I've been using Gilbert Strang's OCW videos[4] on Youtube.
FWIW, I've evolved the way I study math, and what I do now works for me, even though it's 100% not the way you'd ordinarily see suggested. That is, I watch math videos fairly passively and don't work problems at the same time and treat it like being in a class per-se. I used to do the thing of treating it like a class, pausing the video to work examples, and what-not, and that does work. But it's very slow and tedious.
Now, I just watch the videos, acknowledging that I won't absorb everything and that I also need to work problems for long-term retention. So now what I do is watch passively to a certain point (which I determine fairly subjectively) then I stop with the videos for a while, pick up a textbook or one of those "workbook" type books I mentioned earlier, and work problems for a while. Then I review the parts that I find myself struggling with. I'm also just now starting to add "creating Anki cards" as something I do during that second pass.
Once I start getting a decent Anki deck built up, I'll be reviewing that regularly as well to help build retention. I only create cards for things that seem amenable to rote memorization, and TBH, I'm still working on figuring out what things are best to include, and how to structure those cards. What I don't intend to do is include specific problems where all I'd be doing is memorizing the answer to a problem. So far it's just formulas and things are are very obvious candidates to be memorized, and "algorithm" things like the "chain rule" from calculus, and similar.
I will be checking out the ones I don't know, because the ones I do (Weeks, Arnold's classical mechanics and Spivak's differential geometry) are fantastic IMHO
Out of this list, the books I am familiar with, are great (Hilbert-Courant, Spivak, Korner's books). At the same time, even with extensive mathematical training, I haven't read them from start to finish. I wouldn't even like to say "read". For someone who's not used to mathematical reading, some of these books require careful study. That means generating examples to understand results (theorems), trying your own conjectures, proving things yourself etc. Over time, one becomes familiar with most/all the material in a book but the knowledge might have been acquired through various books (and courses) over time.
Also, mathematics is a massive field. The first question would be what kinds of mathematics would you like to get better at. There are great books in analysis. If you are starting out with a solid calculus knowledge, try Abbott's Understanding Analysis [1] or Duren's Invitation to Classical Analysis [2]. For asymptotic methods in PDEs, try Bender and Orszag [3] which is a wonderful book. But again, this might not be your cup of tea at all and there are more abstract or formal books like Rudin's.
If you want to approach fields without a lot of machinery, graph theory books by Bollobas are great (but difficult). See his Modern Graph Theory book [4] as an example.
For linear algebra, one of my favorites (but it was after I already learned the subject) is Trefethen's Numerical Linear Algebra book [5]. Another beautiful topic is at the intersection of linear algebra and combinatorics. See Babai and Frankl's lectures freely available online.
Then there are wonderful topics in geometry. A massive mountain to climb would be algebraic geometry. For one starting point, see [6]. Differential geometry (Spivak's multi-volume work or Needham's differential forms book) is another wonderful area. I would recommend Crane's discrete differential geometry course at Carnegie Mellon [7] if you want a concrete introduction.
You might want to demystify a topic you have heard about. E.g. Galois theory and the unsolvability of quintic equations. You could look at [8] which guides your way through wonderful problems.
We haven't even touched huge swathes of mathematics including anything topological or number theory. Even within the topics mentioned above, once you start, your journey will take a life of its own and you'll encounter multiple books and papers opening up new sub-fields.
The only approach that worked well for me in the past was to get completely consumed by what one topic one was studying. This meant not getting distracted by multiple topics. Once one enters the workforce, this is very hard (or at least has been for me). Without knowing someone, it's hard to recommend anything but the advantage with topics like graph theory and combinatorics is that one needs less machinery (as opposed to something like algebraic geometry). These fields lead you to interesting problems very rapidly and one can wrestle with them part-time.
* Geometry and the imagination by Hilbert and Cohn-Vossen
* Methods of mathematical physics by Courant and Hilbert
* A comprehensive introduction to differential geometry by Spivak (and its little brothers Calculus and Calculus on manifolds)
* Fourier Analysis by Körner
* Arnold's books on ODE, PDE and mathematical physics are breathtakingly beautiful.
* The shape of space by Weeks
* Solid Shape by Koenderink
* Analyse fonctionnelle by Brézis
* Tristan Needhams "visual" books about complex analysis and differential forms
* Information theory, inference, and learning algorithms by MacKay (great book about probability, plus you can download the .tex source and read the funny comments of the author)
And finally, a very old website which is full of mathematical jewels with an incredibly fresh and clear treatment: https://mathpages.com/ ...I'm in love with the tone of these articles, serious and playful at the same time.