Supervised, Unsupervised, Semi-supervised, Reinforcement learning

1. Supervised

Supervised learning procedures are used in problems for which we
can provide the system with example inputs as well as their corre‐
sponding outputs and wish to induce an implicit approximation of
the rules or function that governs these correlations.

The kinds of problems that can be addressed by supervised learning procedures are generally divided into two categories: classification and regression problems.

In a classification problem, the outputs relate to a set of discrete categories.
For example, we may have an image of a handwritten character and
wish to determine which of 26 possible letters it represents. In a
regression problem, the outputs relate to a real-valued number. For
example, based on a set of financial metrics and past performance
data, we may try to guess the future price of a particular stock.

2. Unsupervised

Unsupervised learning procedures do not require a set of known out‐
puts. Instead, the machine is tasked with finding internal patterns
within the training examples. Procedures of this kind are “unsuper‐
vised” in the sense that we do not explicitly indicate what the system
should learn about. Instead, we provide a set of training examples
that we believe contains internal patterns and leave it to the system
to discover those patterns on its own.

In general, unsupervised learning can provide assistance in our efforts to understand extremely complex systems whose internal patterns may be too
complex for humans to discover on their own. Unsupervised learn‐
ing can also be used to produce generative models…

3. Semi-supervised

Semi-supervised learning procedures use the automatic feature dis‐
covery capabilities of unsupervised learning systems to improve the
quality of predictions in a supervised learning problem. Instead of
trying to correlate raw input data with the known outputs, the raw
inputs are first interpreted by an unsupervised system. The unsuper‐
vised system tries to discover internal patterns within the raw input
data, removing some of the noise and helping to bring forward the
most important or indicative features of the data. These distilled ver‐
sions of the data are then handed over to a supervised learning
model, which correlates the distilled inputs with their correspond‐
ing outputs in order to produce a predictive model whose accuracy
is generally far greater than that of a purely supervised learning system.

4. Reinforcement learning

Reinforcement learning procedures use rewards and punishments to
shape the behavior of a system with respect to one or several specific
goals. Unlike supervised and unsupervised learning systems, rein‐
forcement learning systems are not generally trained on an existent
dataset and instead learn primarily from the feedback they gather
through performing actions and observing the consequences.

From Machine Learning for Designers