How to get from high school math to cutting-edge ML/AI
justinmath.com
justinmath.com
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.
You can also get there from the Table of Contents -- to get there, go to your dashboard (where your learning tasks are), click on the name of your course in the top-left, and you'll be brought to a screen that says "Course Sequence" with a list of courses on the left and units on the right. If you expand the units, you'll see topics, and you can click on a topic to view its lesson in reference mode.
Note that XP & knowledge credit is only awarded for completing learning tasks on your dashboard, not for anything you do within a lesson in reference mode.
Thanks for the amazing support and the cool product!
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.
I'm glad that you're having a good experience with the quizzes! That's exactly what we aim for: frequent, low-stakes, broad-coverage quizzes that challenge you to show off what you've learned (instead of stressing you out and overwhelming you). Used properly like this, quizzes are an extremely powerful learning tool. You get the benefits of retrieval practice, the spacing effect, and interleaving, all merged into one. And we pinpoint exactly what skills you need more practice on (which we have you review immediately afterwards), and by carefully calibrating the time constraint, we make sure you're developing your skills fully to the point of automaticity.
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 to answer your question: the math books on that page have "correct answer" solutions where you can tell if you got it right, but not fully-worked-out solutions. Introduction to Algorithms and Machine Learning technically does not have any solutions, but most of the problems involve constructing code implementations that match up with worked examples, or that give a desired result, so you can tell if you got it correct and in many cases you can follow along with the worked example to debug your code if it's not producing the desired output.)
Do you plan to make a proofs based book available as well?
I had fun writing them and I'm glad that they are making a positive impact, but since then I've been consumed by my work on Math Academy, which I find even more fun/impactful. (We do have a Methods of Proof course out, which is many times more scaffolded, refined, comprehensive, and generally instructionally superior to any textbook I could write independently, not to mention it's adaptive.)
So, long story short, I enjoyed writing those textbooks and am glad they're seeing the light of day, but I've moved on to a new chapter of life and don't plan on writing any more math books in the future (with the possible exception of something super niche like the math behind maximizing learning efficiency in hierarchical knowledge structures).
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.