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Any algorithm that uses data (in some form) to improve some performance measure (aka objective function), or to find some function, can be considered a machine learning algorithm. See this answer for more complete definitions of ML. k-means does that. It uses the data to find some division of the data itself into groups, in order to maximize some objective ...

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@The Pointer the $2^n$ came from the question: How many function do we need to have if each of the $n$ inputs can be missing? example: $f_1(\text{missing}, x_2, x_3, \dots, x_n)$ for $x_1$ missing $f_2(x_1, x_2, \text{missing}, x_4, \text{missing}, \dots, x_n)$ for $x_3$ and $x_5$ missing. So this problem is a combinatorial one and the event for each $x_i$ ...

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RLS is a second order optimizer, so, unlike LMS which takes into account an approximation of the derivative of the gradient, RLS also considers the second order derivative. You can study more about second order methods in sub-section "8.6 Approximate Second-Order Methods" of the following book available online: https://www.deeplearningbook.org/...

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