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> If I choose to go Ai route, I do not know from where to start ...

This type of comment is often made in machine learning (ML) related submissions.

The pre-req list is long: calculus, linear algebra, stats, probability, numerical methods (for optimization, linear algebra, maybe interpolation), etc. BUT, you don't really need to go through the entirety of each subject for ML. For example, in calculus, you probably only need to focus on the aspects necessary for optimization, rather than integral techniques, convergence of sequences, etc. The trouble is that it is difficult to know which subtopics of each subject are worth spending time on unless you already know machine learning (or you have the luxury of someone with experience guiding you).

The latter difficulty is compounded by the fact that there seems to be many more resources (at least posted as popular submission on the web) for learning neural nets or learning some specific framework to implement neural networks, than to learn the mathematical and statistical foundations of ML. This is fine -- neural nets are a popular and powerful model, and people like to work on something tangible to get acquainted with a topic.

I wonder if people might enjoy a well-written textbook covering the basic math for ML -- something like, "All the math you missed (but need to know for machine learning)" [1]. I might enjoy working on such an ebook if there was desire for one, but my time is pretty limited (like most).

[1]: https://www.amazon.com/All-Mathematics-You-Missed-Graduate/d...




Here's what I've found along the lines of "mathematics for machine learning":

* DS-GA 1002: Statistical and Mathematical Methods (http://www.cims.nyu.edu/~cfgranda/pages/DSGA1002_fall15/inde...) by Carlos Fernandez-Granda of NYU

(There is a 2016 version of the course with different lectures notes as well.)

* Numerical Algorithms (http://people.csail.mit.edu/jsolomon/share/book/numerical_bo...) by Justin Solomon

* Math for Intelligent Systems, 2016 (https://ipvs.informatik.uni-stuttgart.de/mlr/teaching/maths-...) by Marc Toussaint & Hung Ngo of University Stuttgart

* Math for Intelligent Systems, 2015 (https://ipvs.informatik.uni-stuttgart.de/mlr/teaching/mathem...) by Nathan Ratliff of University Stuttgart

* Mathematics for Inference and Machine Learning (http://wp.doc.ic.ac.uk/sml/teaching/mathematics-for-machine-...) by Stefanos Zafeiriou and Marc Deisenroth of Imperial College London

Most of these are lectures notes. Some are very detailed, but I think there is still space for a completely fleshed out book on the subject.


Thanks, that was the answer I was looking for, you said it much better than I did! When i look at Ai/ML I see a lot of mathematics, not frameworks and programming languages. Anyone can learn to use specific framework or adopt to certain programming language and environment. What concerned me was mathematics wise, since on EE course Math was much more apply oriented, with integration techniques and geometry, not so much about statistics and probability.


Metacademy [1] does a good job of identifying which subtopics of each subject are relevant to ML.

[1] https://metacademy.org/roadmaps/




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