Have been working my way through mathacademy and i feel so motivated. It is almost like a game and i start to feel confident to have a peek at math Olympiad questions. Thanks a lot for your platform.
Love running into Math Academy students in the wild -- thanks for the kind words and I'm so happy that the system is working out for you! After all the work we've put into building this thing, it's the best feeling ever to hear about positive impact it's having on people's lives.
Sounds like you're well on your way to seriously leveling up your math skills, but if you have any questions about how the system works, how to get the most value out of it, the math learning process in general, etc., feel free to reach out anytime.
Very well. One of the things that needs to be explained, or the information made available is why rote computation and timed tests are important. Both of those are weaknesses of mine and it did not help my motivation not understanding why it was needed. I came to the answer on a post of yours somewhere in Reddit and now I actually want to get better at it. The idea I got from you and my experience is:
Timed tests make sure I know and use the optimal approach to solve the exercise. I found this while solving systems of equations, where naively I could carry fractions around making calculation cumbersome and error prone, while an initial analysis trying to find a multiplier that does away with fractions is the right approach. I kept failing until in the review lessons I paid proper attention. Analysis and optimisation is being taught. I had a similar experience about choosing a generic ln or an appropriate base logarithm.
Rote calculation was a tough one to swallow until you said that only through much computation one starts seeing patterns that will be useful later in more abstract manipulation. I agree with it but doing the first chapter of spivak’s calculus that focuses on proving very basic entities with math’s arithmetic axioms helped a lot. It is incredibly simple maths but hammers down the axioms that are then the basis of all manipulations.
Also I like the lack of human interaction upon failure. A machine does not get disappointed when I fail, nor annoyed that today I am having a bad day intellectually. It just fails me and gives me the opportunity to do something else and do repeats until it is successful. It also does not let me continue with faulty understanding because the program needs to be on schedule. It gives me an expectation of completion based on current progress history. If am not happy I can just apply myself better. The price being steep also makes it painful when I see no experience gained in the whole calendar week, further adding a motivation.
On a more personal experience I dread tests and especially timed tests. I am kind of “fortunate” that my nightmares often are about failing exams or not having time to finish them. When doing math academy I always was afraid of the “quiz” tab coming up, especially because I have kids and house chores that sometimes need to attend. Anyway in mathacademy quizzes are so frequent they normalize the act of test taking and I feel more like they are a challenge for me to “show off” then something to dread. This is in contrast to my experience in college where there was one make or break exam for the whole semester: fail and you will need to repeat the whole semester.
To finish I so much wish my kids will be able to benefit from this approach. I have been trying to convince others of the worth of relearning maths as an adult. It gives an analytical mind and teaches intelectual humility. I find myself successful in life yet I still struggle in some tests and tasks.
I normally say if you are in need of some reality checks regarding your intellectual ability just do maths. There is always a problem of extreme simplicity that one cannot solve, and makes you feel dumb.
This is a very comprehensive resource that not only offers the books, but a canned curriculum(Eurisko) that could be customized to your audience. Say, your colleagues at work(considering they came from STEM background with rusty vector calc like me) A question to the OP is, would it be possible for someone like me to "adapt" this curriculum to conduct my own version of "Eurisko"? What are the licensing terms?
Not entirely sure I understand the question, but I'll answer to the best of my understanding: If you'd like to teach a course using one of my textbooks, then go ahead -- it's freely available online so that anyone who wants to learn from it can do so. All I ask is that if you pull readings/problems from the book, you cite the source, like how it's done when a professor pieces together a course using different readings/problems from different textbooks.
But, to clarify: the Eurisko book (Introduction to Algorithms and Machine Learning) only covers a small slice of the total roadmap from high school math to cutting-edge ML/AI. The other resources I recommend are the ones I mention in this post.
Thanks for taking the time to answer. And you did answer the core of my question. Indeed, i will be citing the books as the source. Thanks again for making this resource available for free.
if the aim is to just read and understand the papers, it is just a matter of learning the ap calc and some first-year university maths to get the basics out of the way.
the rest of the journey is to find an ml course which acts like a survey of the current state of the art. this field has complexity due to abstraction and horrendous naming practices. to understand a given paper requires working your way in reverse from concepts around it.
in addition, learn a "maths in code" platform of your choice to map the concepts to something you can run.
I feeling like you described a “draw the rest of the owl” situation with “find an ml course”.
I’m basically in this situation, I’ve implemented products around AI and ML. I understand them at a practical level but want to dive into the theoreticals more. Finding a “survey ML course” is a huge challenge for me. I have absolutely no idea what’s considered state of the art.
however, the strategy that should give good results is going for the open-source coursework from one of the major universities. these courses may lag a semester or two from SOTA, but they often give a good overview. pick your poison and go from there. for example the above link was found the same way.
That's Stage 1: Foundational Math -- with the addition of calculus-based probability/statistics, and the specification that it's the multivariable chain rule (not just the one in single-variable calculus), as well as differential multivariable calculus in general (gradient vector, etc.).
But there's plenty more to learn in stages 2/3/4 before you reach the cutting edge. Would encourage you to read the article! :)
Should also point out that -- in the USA, at least -- linear algebra, multivariable calc, and calculus-based probability/statistics are not typically taught in high school. There's quite a bit of university-level math that someone needs to know if they want to reach cutting-edge ML/AI.
Not many. I went to a good school but still had to wait to first-year university to learn linear algebra. Assuming you mean "real" linear algebra with matrices, inverse matrices, determinants (please don't mention Axler's book), and linear operators.
I believe Linear algebra is/was taught in the pre-calc path of the provincial public curriculum (Canada) if you chose it as an optional thing. I did not.
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Sounds like you're well on your way to seriously leveling up your math skills, but if you have any questions about how the system works, how to get the most value out of it, the math learning process in general, etc., feel free to reach out anytime.
Also I like the lack of human interaction upon failure. A machine does not get disappointed when I fail, nor annoyed that today I am having a bad day intellectually. It just fails me and gives me the opportunity to do something else and do repeats until it is successful. It also does not let me continue with faulty understanding because the program needs to be on schedule. It gives me an expectation of completion based on current progress history. If am not happy I can just apply myself better. The price being steep also makes it painful when I see no experience gained in the whole calendar week, further adding a motivation.
On a more personal experience I dread tests and especially timed tests. I am kind of “fortunate” that my nightmares often are about failing exams or not having time to finish them. When doing math academy I always was afraid of the “quiz” tab coming up, especially because I have kids and house chores that sometimes need to attend. Anyway in mathacademy quizzes are so frequent they normalize the act of test taking and I feel more like they are a challenge for me to “show off” then something to dread. This is in contrast to my experience in college where there was one make or break exam for the whole semester: fail and you will need to repeat the whole semester.
To finish I so much wish my kids will be able to benefit from this approach. I have been trying to convince others of the worth of relearning maths as an adult. It gives an analytical mind and teaches intelectual humility. I find myself successful in life yet I still struggle in some tests and tasks.
I normally say if you are in need of some reality checks regarding your intellectual ability just do maths. There is always a problem of extreme simplicity that one cannot solve, and makes you feel dumb.
But, to clarify: the Eurisko book (Introduction to Algorithms and Machine Learning) only covers a small slice of the total roadmap from high school math to cutting-edge ML/AI. The other resources I recommend are the ones I mention in this post.
the rest of the journey is to find an ml course which acts like a survey of the current state of the art. this field has complexity due to abstraction and horrendous naming practices. to understand a given paper requires working your way in reverse from concepts around it.
in addition, learn a "maths in code" platform of your choice to map the concepts to something you can run.
I’m basically in this situation, I’ve implemented products around AI and ML. I understand them at a practical level but want to dive into the theoreticals more. Finding a “survey ML course” is a huge challenge for me. I have absolutely no idea what’s considered state of the art.
i personally found this course to be a good place for deep learning (again not a survey course that covers classical ML for context) - https://uvadlc-notebooks.readthedocs.io/en/latest/index.html
however, the strategy that should give good results is going for the open-source coursework from one of the major universities. these courses may lag a semester or two from SOTA, but they often give a good overview. pick your poison and go from there. for example the above link was found the same way.
But there's plenty more to learn in stages 2/3/4 before you reach the cutting edge. Would encourage you to read the article! :)
Should also point out that -- in the USA, at least -- linear algebra, multivariable calc, and calculus-based probability/statistics are not typically taught in high school. There's quite a bit of university-level math that someone needs to know if they want to reach cutting-edge ML/AI.