Introduction to Machine Learning

Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.

Problems and Tasks of ML

Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system:

• In classification, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one (or multi-label classification) or more of these classes.Like:  "spam" and "not spam".
• In regression, also a supervised problem, the outputs are continuous rather than discrete.
• In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.
• Density estimation finds the distribution of inputs in some space.
• Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space.
• Topic modeling is a related problem, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics.

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A core objective of a learner is to generalize from its experience.

Generalization:

• in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set.
• The training examples come from some generally unknown probability distribution and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

=======================================================Reference:  https://en.wikipedia.org/wiki/Machine_learning