Learning paradigms

ML algorithms can be classified based on the method they use as follows:

• Probabilistic versus non-probabilistic
• Modeling versus optimization
• Supervised versus unsupervised

In this book, we classify our ML algorithms as supervised versus unsupervised. The distinction between these two depends on how the model learns and the type of data that's provided to the model to learn:

• Supervised learning: Let's say I give you a series and ask you to predict the next element:

(1, 4, 9, 16, 25,...)

You guessed right: the next number will be 36, followed by 49 and so on. This is supervised learning, also called learning by example; you weren't told that the series represents the square of positive integers—you were able to guess it from the five examples provided.

 

In a similar manner, in supervised learning, the machine learns from example. It's provided with a training data consisting of a set of pairs (X, Y) where X is the input (it can be a single number or an input value with a large number...