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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.

by (108k points)

In supervised algorithms, the classes are predetermined. These classes can be understood as a finite set, previously arrived at by a human. In usage, a certain segment of data will be labeled with these classifications. The machine learner's task is to explore patterns and construct mathematical models. These models then are evaluated based on their predictive capacity to measures of variance in the data itself. Many of the techniques that are referred to in the documentation (decision tree induction, naive Bayes, etc) are examples of supervised learning techniques.

Unsupervised learners are not provided with classifications. The primary task of unsupervised learning is to develop classification labels automatically. Unsupervised algorithms seek out the similarity between pieces of data to determine whether they can be characterized as forming a group. These groups are termed clusters, and there is a whole family of clustering machine learning techniques.

In reinforcement learning model is continuous gets updated based on processed data and the result. For example, a 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, next analyze new position and update move parameters, so that the next move would be more accurate (repeat until get to point B).

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

If you wish to learn about Supervised Learning and Unsupervised Learning then visit these Types of Machine learning.