$1$ Machine learning

You have an internship at an electronics supply distributer just south of Dallas.

Today an errant forklift caused $11$ bins of electrical connectors to fall off of their

shelves in the ware$-$house into the aisle spilling and intermixing their entire

contents. Each of the bins con$-$tained hundreds of one type of connector. Since

you just started your position, your man$-$ager suggested you clean up the mess.

Because you know they would like to sell all of the undamaged parts recovered in

the accident, you decide the first step in recovery should be to separate the mess

into separate piles by color, then for each of those piles separate the connectors

into smaller piles based on differences in size. Finally you plan, for each pile, to

place all identical parts into a new bin.

What kind of learning are you practicing $($super$-$vised or unsupervised$)?$

Will this process result with $11$ bins with the same number of parts of the

same types as were in each bin before the accident? $(1$ ssume no parts are

lost in the accident$).$ Justify your answer.

If the original $11$ bins that spilled had a detailed picture of one of the parts

and the length of the connectors that were originally contained in that bin,

could that change the type of learning that you would be practicing? If

so$,$ to what learning type?