Differential Machine Learning : novel ML algorithms with unreasonable effectiveness for pricing and risk approximations in finance

[Dean]

Nice summary, fact-based survey for current stat learning/ML path.

I cannot agree "There is an indication the ESL is frequentist and Bishop is Bayesian and economics tends toward following a Bayesian approach to modeling?" ESL is both frequentist and Bayesian, furthermore, everything is Bayesian in today's statistic world.

IMHO, ESL is math/stat approach, Bishop and Murphy is computer science approach (maybe Bishop is a mix). I've encountered quite a lot of approximations when I do ML methods, many of them are spontaneous, random and lack of math/stat theory backup. However, they somehow come up with acceptable estimation results under certain situations. I think they are popular because they are trying to tackle some complicated models which traditional math/stat methods cannot handle, with sacrificing accuracy and reliability.

I'm still trying to figure out how to balance those two approaches. Be open to ML methods, keep an eye on those new development on ML, but always beware of their shortcomings. Personally, I feel more comfortable only using ML methods as first time screening tools.

Nice summary, fact-based survey for current stat learning/ML path.

I cannot agree "There is an indication the ESL is frequentist and Bishop is Bayesian and economics tends toward following a Bayesian approach to modeling?" ESL is both frequentist and Bayesian, furthermore, everything is Bayesian in today's statistic world.

IMHO, ESL is math/stat approach, Bishop and Murphy is computer science approach (maybe Bishop is a mix). I've encountered quite a lot of approximations when I do ML methods, many of them are spontaneous, random and lack of math/stat theory backup. However, they somehow come up with acceptable estimation results under certain situations. I think they are popular because they are trying to tackle some complicated models which traditional math/stat methods cannot handle, with sacrificing accuracy and reliability.

I'm still trying to figure out how to balance those two approaches. Be open to ML methods, keep an eye on those new development on ML, but always beware of their shortcomings. Personally, I feel more comfortable only using ML methods as first time screening tools.

Thank you for the reply. It sounds like you are speaking with industry experience and thus can cut to the chase as to the required background. My background is as a mathematician and statistician.

What is "IMHO"?

It is my understanding that the prerequisites for the texts "Machine Leaning and Pattern Recognition", Bishop and "Elements of Statistical Learning" are illustrated in the Cambridge and Stanford's course descriptions, respectively.

Department of Computer Science and Technology – Course pages 2019–20: Machine Learning and Bayesian Inference

Statistics 315a Home page

Hope this helps clarify my post.
Kind regards