Supervised Learning, (ii) Unsupervised Learning, (iii) Reinforcement Learn

Question

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. In order to do this, the developers obtain exact coin specications 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.

Answer 1

(i) unsupervised learning - as no labelled data is available

(ii) supervised learning - as you already have labelled data available

(iii) reinforcement learning- where you learn and relearn based on the actions and the effects/rewards from that actions.

Answer 2

Let's say, you have dataset represented as matrix X. Each row in X is an observation (instance) and each column represents particular variable (feature).

If you also have (and use) vector y of labels, corresponding to observations, then this is a task of supervised learning. There's "supervisor" involved, that says which observations belong to class #1, which to class #2, etc.

If you don't have labels for observations, then you have to make decisions based on the X dataset itself. For example, in the example with coins you may want to build model of normal distribution for coin parameters and create system that signals when the coin has unusual parameters (and thus may be attempted fraud). In this case you don't have any kind of supervisor that would say what coins are ok and what represent fraud attempt. Thus, it is unsupervised learning task.

In 2 previous examples you first trained your model and then used it, without any further changes to the model. In reinforcement learning model is continuously improved based on processed data and the result. For example, robot that seeks to find the way from point A to point B may first compute parameters of the move, then shift based on these parameters, then analyze new position and update move parameters, so that next move would be more accurate (repeat until get to point B).

Based on this, I'm pretty sure you will be able to find correspondence between these 3 kinds of learning and your items.