In the meantime, the following might help. But from experience, if your math is already shaky, you would certainly need to look for resources covering everything listed in a more in depth manner.
Basic calculus refresher - http://www.stat.wisc.edu/~ifischer/calculus.pdf
Learning from Data - http://www.inf.ed.ac.uk/teaching/courses/fmcs1/readings/matl...
Basic probability theory - http://homepages.inf.ed.ac.uk/sgwater/teaching/general/proba...
Mathematics for Machine Learning - https://pdfs.semanticscholar.org/910e/3118b50f426e5e840561e1...
Math for Machine Learning - https://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf
Thank you for posting these links. I'll definitely go through these.
That’s a pretty discouraging statement, so let me try to be more helpful. The mathematics comprising contemporary machine learning and data analysis primarily consists of linear algebra, calculus, mathematical statistics and, at the high end, probability theory. There are boutique efforts to use things like topology but that’s non-standard.
As it stands, your question is underspecified, which is what I repeat for most of these questions. The answers you receive here are going to variously interpret what you’re actually looking for and prescribe based on the author’s interpretation and intuition. It would be more useful if you explained exactly what your goals are. Here’s precisely the problem: you apparently don’t know these mathematics already, but you’re asking for something that “doesn’t necessarily go into depth.” How much depth is too much, and how would you know that exactly given your present unknown unknowns? If you explain what you actually want to achieve, we might be able to
1. Tell you that you don’t actually need that “top level view” to achieve what you’d like,
2. Tell you that such a top level view is not nearly enough for what you’d like to do, or
3. Tell you that a top level view is coherent, and optimize the best materials for you to learn from based on what you want to do.
It would also help us make recommendations if you explained what your current level of mathematical background looks like. Have you taken linear algebra at least once? Exactly how basic do our resources have to be? Different textbooks written for different audiences can variously explain the same concept in two pages or 10 pages, and they can emphasize different things.
I’d like to help, and I probably can, but it would go a long way if you could tell us what your end goal is instead of what you interpret as the next step towards that goal. Then we can provide resources based on your mathematical maturity.
In a nutshell, you take probability distributions and project them onto topological manifolds, such that the distribution consists of points on the manifold. You can find more by searching for key words like "information geometry" or "differential geometry machine learning".
Obviously I know very little about these areas and that is why I posted such a question. By posing this question, I didn't mean that I wanted to 'get there' quickly. In fact I am in no hurry and willing to go as deep as possible.
What I actually mean to ask is that I was looking for a kind of syllabus/crash courses so that I can get an idea about what kind of mathematics is involved in these areas. Based on that, I'll be able to start my studies in much more organized way. Also, from there on, I myself be able to figure out how to look for further resources. This is the style of learning I usually follow.
Hope you understand my point.
[1]: https://www.coursera.org/specializations/mathematics-machine...
There is the financial aid and normally there is the option to watch the lectures and see the assignment for free. On the other hand, the cost of the courses rarely exceeds $50.
Previews are free though, and might be useful if all you need is an overview: https://minireference.com/static/excerpts/noBSguide_v5_previ... https://minireference.com/static/excerpts/noBSguide2LA_previ...
This is also good: http://ml-cheatsheet.readthedocs.io/en/latest/
Avrim Blum, John Hopcroft, and Ravindran Kannan
Copyright 2015
Stats thereafter is pie, ML low hanging fruit will come easily.
Good inference and learning from data on the other hand is experience: expect years, no shortcuts. In fact, suffer. It’ll make the journey as informative as possible.
Really, if you're not going to go to e.g. community college where you can do a lot of this (LA, prob/stats, calc) your best shot is to find study groups in Data science meetups, etc which are plentiful in major cities since many people have the same impulse. Truthfully, self study rarely gets far if people never went beyond Calc 2.
Here's a blog about one guy's adventure, read the part where he talks about Shores' LA text(not a bad book by the way but yes, lots of typos): https://news.ycombinator.com/item?id=8996024
Week 2 Calculus https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53...
Week 3 Probability https://www.edx.org/course/introduction-probability-science-...
Week 4 Algorithms https://www.coursera.org/courses?languages=en&query=Algorith...
* http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.p... * http://web4.cs.ucl.ac.uk/staff/s.prince/book/book.pdf * http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%...
I am not an expert in ML. I have heard experts in ML call the above books introductory.
https://github.com/llSourcell/Learn_Machine_Learning_in_3_Mo...
First month covers the basics (calculus, algebra, probability, algorithms)
Second month is focused around coding (Data science in Python) and putting the things learned in the first month in a ML context (Siraj's Math of Intelligence course on YouTube)
Third month is all around Deep Learning.
http://parrt.cs.usfca.edu/doc/matrix-calculus/index.html#sec...
The rest will be a cake walk.
But you need LA from matrices to spectral decomposition and Calc from differentiation to Laplace transforms; so no easy feat. You'll need a fair amount of "give a damn" to put yourself through it.