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Depending upon whether or not data is labeled, a different family of algorithms is applied. The four main
types of Machine Learning are Supervised Learning, Unsupervised Learning, Semi-supervised Learning,
and Reinforcment Learning.

Supervised Learning
‘Supervised’ means that, before the algorithm processes the training data, a supervisor (which may be a
human, group of humans, or a different software system) has accurately labeled each of the data inputs
with its correct associated output.
For example, if the goal of the AI system is to correctly classify the objects in different images as either
‘cat’ or ‘dog’, the labeled training data would have image examples paired with the correct classification
label. Supervised Learning systems can also be used for identifying the correct labels of continuous
numerical outputs. For example, ‘given this wing shape input, predict the output air drag coefficient’.

Unsupervised Learning
Unsupervised algorithms are those that can
extract features from the data without the
need for a label for the results. Using the
aforementioned example of an image classifier,
the AI model produced by an unsupervised
algorithm would not return that a specific input
image was of a ‘cat’ or a ‘dog’. Rather, the model
would sort the training dataset into various
clusters based on their similarity. One sorted
cluster might be the desired groups of cats and
dogs, but images might instead be clustered
based on undesired categories such as whether
or not they have a blue sky in the background,
or a wooden floor.
Unsupervised Learning systems are therefore
often less predictable, but because unlabeled
data is almost always more available than labeled
data, they remain critical. Additionally, Unsupervised algorithms are very useful when developers seek
to explore and understand their own datasets and what properties might be useful in either developing
automation or changing operational practices and policies.

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