Why machine learning is an optimization problem?

Specifically, you learned: Machine learning algorithms perform function approximation, which is solved using function optimization. Function optimization is the reason why we minimize error, cost, or loss when fitting a machine learning algorithm.

What is convex vs non-convex?

Non-convex. A polygon is convex if all the interior angles are less than 180 degrees. If one or more of the interior angles is more than 180 degrees the polygon is non-convex (or concave).

Why isn’t convex optimization more interesting for machine learning?

One could argue that convex optimization shouldn’t be that interesting for machine learning since instead of dealing with convex functions, we often encounter loss surfaces like the one below, that are far from convex. (source: https://www.cs.umd.edu/~tomg/projects/landscapes/ and arXiv:1712.09913)

What is the most interesting thing to learn in machine learning?

The most interesting thing you would first come across when starting out with machine learning is the optimization algorithm and to be specific, it is the gradient descent, which is a first-order iterative optimization algorithm used to minimize the cost function.

What is the formula for minimizing µ1 in machine learning?

1+exp(wTxi−wTxj) ◮k-means: minimize µ1,…,µk J(µ) = Xk j=1 X i∈Cj kxi−µjk 2 ◮And more (graphical models, feature selection, active learning, control) Duchi (UC Berkeley) Convex Optimization for Machine Learning Fall 2009 6 / 53 What is Optimization But generally speaking… We’re screwed.

How to solve the problem of momentum in machine learning?

One way to solve this issue is by using the concept momentum. Convex functions play a huge role in optimization. Optimization is the core of a machine learning model. Understanding convexity is really important for the same, which I believe you did from this article.