A Cookbook for Machine Learning: Vol 1

Re:A Cookbook for Machine Learning: Vol 1 by keont on Friday November 17, 2017 @03:39AM (#15720246)

Yep. Second formulae and I'm out of my league. Is it just me that gets frustrated when things aren't explained? What do I have to study to be able to read this

θ∗,ψ∗←argminθ,ψg(θ,ψ)

With no prior explanation and say, yep, now that you put it that way, it's true?

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Re:A Cookbook for Machine Learning: Vol 1 by pedrosorio on Friday November 17, 2017 @03:46AM (#15720269)

Notation is notation. I think it's hard to come up with a single guide because a lot of conventions depend on the area.

In the context of optimization, a star refers to the value of the variable that minimizes (or maximizes) a function you're interested in.

You will also see a hat over a variable (https://en.wikipedia.org/wiki/Hat_operator#Estimated_value) sometimes (from statistics) which is an estimate of some quantity from data (and that estimate may be obtained by minimizing a function - solving an optimization problem)

By the way, this is the first result when googling argmin: https://en.wikipedia.org/wiki/Arg_max

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Re:A Cookbook for Machine Learning: Vol 1 by zardo on Friday November 17, 2017 @10:25AM (#15722660)

This isn't a formula that is 'true', it's a minimally specified program.

It takes a function g, and returns the values of it's arguments (theta and psi) that minimize the function g.

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Re:A Cookbook for Machine Learning: Vol 1 by master_yoda_1 on Friday November 17, 2017 @01:36PM (#15724694)

This might help https://en.wikipedia.org/wiki/List_of_mathematical_symbols

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