What types of Machine Learning, if any, best describe the following three

scenarios: $(2\mathrm{.}5)$

$($i$)$ A coin classification system is created for a vending machine. The developers obtain exact coin

specifications from the U$.$S$.$ Mint and derive a statistical model of the size, weight, and denomination, which

the vending machine then uses to classify coins.

$($ii$)$ Instead of calling the U$.$S$.$ Mint to obtain coin information, an algorithm is presented with a large set of

labeled coins. The algorithm uses this data to infer decision boundaries which the vending machine then uses

to classify its coins.

$($iii$)$ A computer develops a strategy for playing Tic$-$Tac$-$Toe by playing repeatedly and adjusting its strategy

by penalizing moves that eventually lead to losing.

$[$a$]($i$)$ Supervised Learning, $($ii$)$ Unsupervised Learning, $($iii$)$ Reinforcement Learning

$[$b$]($i$)$ Supervised Learning, $($ii$)$ Not learning, $($iii$)$ Unsupervised Learning

$[$c$]($i$)$ Not learning, $($ii$)$ Reinforcement Learning, $($iii$)$ Supervised Learning

$[$d$]($i$)$ Not learning, $($ii$)$ Supervised Learning, $($iii$)$ Reinforcement Learning

$[$e$]($i$)$ Supervised Learning, $($ii$)$ Reinforcement Learning, $($iii$)$ Unsupervised L