I am new to Machine learning. While reading about Supervised Learning, Unsupervised Learning, Reinforcement Learning I came across a question as below and got confused. Please help me in identifying in below three which one is Supervised Learning, Unsupervised Learning, Reinforcement Learning.
What types of learning, if any, best describe the following three scenarios:
(i) A coin classification system is created for a vending machine. To do this, 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 its 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.