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I wanted to know how much of machine learning requires optimization. From what I've heard statistics is an important mathematical topic for people working with machine learning. Similarly how important is it for someone working with machine learning to learn about convex or non-convex optimization?

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    $\begingroup$ "working with machine learning" is a vague concept - working to develop better ML methods will mean one answer, developing ML systems that use known methods is an entirely different thing. $\endgroup$ – Peteris Jun 19 '17 at 14:39
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The way I look at it is that statistics / machine learning tells you what you should be optimizing, and optimization is how you actually do so.

For example, consider linear regression with $Y = X\beta + \varepsilon$ where $E(\varepsilon) = 0$ and $Var(\varepsilon) = \sigma^2I$. Statistics tells us that this is (often) a good model, but we find our actual estimate $\hat \beta$ by solving an optimization problem

$$ \hat \beta = \textrm{argmin}_{b \in \mathbb R^p} ||Y - Xb||^2. $$

The properties of $\hat \beta$ are known to us through statistics so we know that this is a good optimization problem to solve. In this case it is an easy optimization but this still shows the general principle.

More generally, much of machine learning can be viewed as solving $$ \hat f = \textrm{argmin}_{f \in \mathscr F} \frac 1n \sum_{i=1}^n L(y_i, f(x_i)) $$ where I'm writing this without regularization but that could easily be added.

A huge amount of research in statistical learning theory (SLT) has studied the properties of these argminima, whether or not they are asymptotically optimal, how they relate to the complexity of $\mathscr F$, and many other such things. But when you actually want to get $\hat f$, often you end up with a difficult optimization and it's a whole separate set of people who study that problem. I think the history of SVM is a good example here. We have the SLT people like Vapnik and Cortes (and many others) who showed how SVM is a good optimization problem to solve. But then it was others like John Platt and the LIBSVM authors who made this feasible in practice.

To answer your exact question, knowing some optimization is certainly helpful but generally no one is an expert in all these areas so you learn as much as you can but some aspects will always be something of a black box to you. Maybe you haven't properly studied the SLT results behind your favorite ML algorithm, or maybe you don't know the inner workings of the optimizer you're using. It's a lifelong journey.

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In practice, a lot of packages take care of the optimization and most of the math details for you. For example, TensorFlow can do backprop+stochastic gradient descent for training neural nets for you automatically (you just have to specify learning rate). scikit-learn's ML tools will generally not require you to actually know stuff about how the optimization actually occurs, but maybe just set some tuning parameters and it takes care of the rest (e.g. number of iterations the optimizer runs for). For example, you can train a SVM without knowing any math in scikit-learn-- just feed in the data, kernel type, and move on.

That being said, knowing basic optimization (e.g. at the level of Boyd and Vandenberghe's Convex Optimization / Bertsekas' Nonlinear programming) can be helpful in algorithm / problem design and analysis, especially if you're working on theory stuff. Or, implementing the optimization algorithms yourself.

Note that the textbook optimization methods often need tweaks to actually work in practice in modern settings; for example, you might not use classic Robbins-Munroe stochastic gradient descent, but a faster accelerated variant. Nevertheless, you can gain some insights from working with the optimization problems.

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