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Ask HN: What maths are critical to pursuing ML/AI?

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Re: Ask HN: What maths are critical to pursuing ML/AI?

#131

Post by zimzim »

jonahx wrote: post #124
mindcrime wrote: post #9

It depends on how deep you want to go and what your goals are, but I'd say that CuriouslyC pretty much nailed it. Multi-variable calculus, linear algebra, and probability / stats are definitely the core. If you're interested in finding more "freely available online" maths references, check out: http://people.math.gatech.edu/~cain/textbooks/onlinebooks.ht... http://www.openculture.com/free-math-textbooks https://open.…

Another upvote for 3blue1brown. I just watched his linear algebra series and it's probably the most outstanding math instruction I've encountered.

https://www.youtube.com/user/EugeneKhutoryansky

another nice yt channel about math and physics.

Re: Ask HN: What maths are critical to pursuing ML/AI?

#132

Post by pramalin »

I found this study plan very useful for me. https://www.analyticsvidhya.com/blog/2017/01/the-most-compre...

Provides a very good idea of the courses required and their time frame. I roughly followed along this path but took "Analytics Edge" https://www.edx.org/course/analytics-edge-mitx-15-071x-3 for introduction into ML algorithms.

Re: Ask HN: What maths are critical to pursuing ML/AI?

#133

Post by tel »

mindcrime wrote: post #77
digitalzombie wrote: post #26

Earlier quoted context omitted.

> "Bayesian Data Analysis" by Andrew Gelman is another great read. If you want to read that book you need real analysis more specifically measure theory (unless that subject is in probability theory for you). You cannot get into the last few chapters without it. Dirichlet Process are described using measures. I don't believe you need multivar calc or info theory. Info theory stuff are used but not as often. I believe…

What's required as a prereq to Measure Theory? Any suggestions on good resources for learning Measure Theory? I have a vague notion that Probability and Measure Theory are intertwined / related somehow, but have never studied the latter specifically.

Probability theory is the study of distributions of constant measure in measure theoretic terms. There are some good resources that mtzet mentions, but I just wanted to note that a lot of the integration terminology which you take for granted reading about probability theory is formally defined in measure theory. It's also very nice for making signal processing math more formal.

Re: Ask HN: What maths are critical to pursuing ML/AI?

#134

Post by yorwba »

laurus wrote: post #88
graycat wrote: post #71

Earlier quoted context omitted.

Part II (2) Linear Algebra (2.1) Linear Equations The start of linear algebra was seen in high school algebra, solving systems of linear equations. E.g., we seek numerical values of x and y so that 3 x - 2 y = 7 -x + 2 y = 8 So, that is two equations in the two unknowns x and y. Well, for positive integers m and n, we can have m linear ( linear is in the above example but omitting here a careful definition) equations…

I have been inspired by some of your past posts suggesting a path for studying mathematics and doing graduate level work, and have changed my direction to try and follow what you suggest. Is there any way I can get in touch with you privately? (I'm not looking for help with specific technical questions if you're concerned about that.)

Are you doing the "Get the book. Read the book. Do the exercises." method? If you are, what's your experience?

I have had some books stored up since forever, and graycat's post did motivate me to finally get around to reading them, but I find it hard to integrate into my daily routine. His 24h challenge killed my productivity for a day, and I can't really afford to get distracted by some tricky proof when I'm supposed to do something else.

Re: Ask HN: What maths are critical to pursuing ML/AI?

#135

Post by WhitneyLand »

PeterisP wrote: post #72
WhitneyLand wrote: post #52

Surprising level of disagreement here on a few items for a sub field that has its own degree tracks. Multiavariable calc you either "abolsutely" need or don't really need. Should be well versed in graph theory, or don't need it much. Surely some of the contradiction is caused by different assumptions of what the goal is. But some of its hard to relate to as a reader. For example, I haven't been in the field but but h…

To apply known methods in cases where they mostly work, you don't need to know the math behind them, you just need to know basic stats and basic probability to interpret the results. So if the assumption is that you'll simply be solving your problems by applying the known methods using the (great!) tooling made by others, then you don't need the math background; you can certainly train undergrads to solve quite nifty…

I think it's more nuanced. On average the better grasp of the theory an engineer has, the more pathways to success they have. Making better decisions, less guessing, leading a team, wanting to have input into future products and services, and so on.

Just having things be less opaque reduces cognitive load, makes more room for creative solutions.

Re: Ask HN: What maths are critical to pursuing ML/AI?

#137

Post by laurus »

yorwba wrote: post #134
laurus wrote: post #88

Earlier quoted context omitted.

I have been inspired by some of your past posts suggesting a path for studying mathematics and doing graduate level work, and have changed my direction to try and follow what you suggest. Is there any way I can get in touch with you privately? (I'm not looking for help with specific technical questions if you're concerned about that.)

Are you doing the "Get the book. Read the book. Do the exercises." method? If you are, what's your experience? I have had some books stored up since forever, and graycat's post did motivate me to finally get around to reading them, but I find it hard to integrate into my daily routine. His 24h challenge killed my productivity for a day, and I can't really afford to get distracted by some tricky proof when I'm suppose…

Yes, I'm working through a few books that way. I didn't see his 24h challenge so I'm not sure what it is, but what has been effective for me is blocking off a few hours every day to work on this stuff. I haven't gotten to the really difficult material he's talking about yet, but I'm looking forward to seeing how this goes. Good luck to both of us!

Re: Ask HN: What maths are critical to pursuing ML/AI?

#138

Post by yorwba »

laurus wrote: post #137
yorwba wrote: post #134

Earlier quoted context omitted.

Are you doing the "Get the book. Read the book. Do the exercises." method? If you are, what's your experience? I have had some books stored up since forever, and graycat's post did motivate me to finally get around to reading them, but I find it hard to integrate into my daily routine. His 24h challenge killed my productivity for a day, and I can't really afford to get distracted by some tricky proof when I'm suppose…

Yes, I'm working through a few books that way. I didn't see his 24h challenge so I'm not sure what it is, but what has been effective for me is blocking off a few hours every day to work on this stuff. I haven't gotten to the really difficult material he's talking about yet, but I'm looking forward to seeing how this goes. Good luck to both of us!

The exercises are here https://news.ycombinator.com/item?id=15022458 (that post was downvoted & flagged to death, so you might have to turn on showdead in your profile to see it)

In a different comment chain on the same submission (https://news.ycombinator.com/item?id=15024640), he challenged the commenters disagreeing with him to do these exercises in 24 hours. The tone was pretty abrasive, TBH, but I found the questions interesting enough that I tackled them in earnest.

I posted my solution attempts, so don't scroll down too far if you want to try them on your own ;)

Re: Ask HN: What maths are critical to pursuing ML/AI?

#139

Post by nerdponx »

Jasamba wrote: post #126
nerdponx wrote: post #121

Earlier quoted context omitted.

Thanks for clarifying. These tools do, however, have an important place in saving practitioners time and energy on the "knob-twiddling". It's a little like robot-assisted surgery: the robot doesn't actually do the surgery, but it makes the surgeon's job a whole lot easier.

That is making the assumption that the person using the tool is a surgeon (an expert in the field who could function independently if needed) which is not who the targeted demographic of such tools is. No-one who understands ML to some non-zero extent would use a plug-and-play ML tool, given that there is ML left to do otherwise. A better analogy would be a janitor activating the red button of the robot machine, whic…

Perhaps, but the meta/hyper-optimization techniques used to implement TPOT, AutoML, etc. are perfectly valid replacements for grid search and stepwise feature selection.
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