Pragmatic Programming Techniques

In classical prediction use case, the predicted output is either a number (for regression) or category (for classification).  A set of training data (x, y) where x is the input and y is the labeled output is provided to train a parameterized predictive...

There are basically 3 different types of Machine Learning
Supervised Learning:  The major use case is Prediction.  We provide a set of training data including the input and output, then train a model that can predict output from an unseen input.
Unsupervised Learning:  The major use case is Pattern extraction.  We provide a set of data that has no output, the algorithm will try to extract the underlying non-trivial structure within the data....

For a classification problem (let say output is one of the labels R, G, B), how do we predict ?
There are two formats that we can report our prediction
Output a single value which is most probable outcome.  e.g. output "B"  if P(B) > P(R) and P(B) > P(G)...

AI is not a new term, it is multiple decades old starting around early 80s when computer scientist design algorithms that can "learn" and "mimic human behavior".
On the "learning" side, the most significant algorithm is Neural Network, which is not very successful due to overfitting (the model is too powerful but not enough data).  Nevertheless, in some more specific tasks, the idea of "using data to fit a function...