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[D] Streamlined ML curriculum to get from zero to research as quickly as possible

As a PhD student of ML, I recently came into realization that most of the things I learned in college and required courses in PhD weren’t really necessary or useful for research in most areas of ML, and that I learned more relevant things outside of the curriculum by myself by reading recent papers. I think the prerequisite for most papers is usually limited to very few number of easily learnable topics and the cited recent papers, so I think more emphasis should be put on reading recent papers than textbooks of rather irrelevant topics.

I believe it is more efficient to learn whatever you think is necessary during your research rather than learning various things beforehand. So, rather than taking various CS & ML courses and then beginning the research, I believe it is better for people to begin research (e.g. reading the recent papers, implementing various ideas) as soon as possible. This way, while doing your research you would specialize to some specific fields and may find lack of some required knowledge. Then, you can take a course necessary for understanding it or just study it on your own if that works, since that’s what researchers usually do. Meanwhile, you can keep reading the recent papers, implement your ideas and accumulate your knowledge of things you cannot learn from textbooks or lectures.

The target of this curriculum is assumed to know at least single-variable calculus (if you know more, you can skip the topics you know!). This includes some advanced high school students. Since most researchers tend to have been a strong student, I set the pace of the curriculum fast. But it can be slowed down. A sample syllabus is provided for each course (taken from MITOCW and Stanford).

1st semester: Multi-variable Calculus [1], Linear Algebra [2], Elementary Probability & Statistics with emphasis on ML [3] (The syllabii should be modified to focus on ML and incorporate Python & Numpy use.)

2nd semester: Classical ML (covering various classical models quickly) [4], DNN course (focusing on CNN and Transformer (w/ pytorch impl.) with literature review mainly on post 2017 papers at the end) (modified ver. of [5, 6]), some supplementary CS course (covering various miscellaneous things you absolutely need to know).

After these semesters, you would have an understanding of what to specialize on and create your own curriculum. For some of them you need to take some more courses first, whereas others can be studied only by reading papers and/or github libraries. Check daily arxiv feed, check recent papers on twitter/reddit, do literature search, implement your ideas etc.

It is curious to me if advanced high school students would be able to pass this curriculum and do research in a year?

Anyway, I hope I can get any feedback on my post. Thank you for reading.

[1] https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm

[2] https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/

[3] https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/

[4] http://cs229.stanford.edu/

[5] https://cs230.stanford.edu

[6] http://vision.stanford.edu/teaching/cs231n/

submitted by /u/Aran_Komatsuzaki
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.