summarise this into 3 bullet points:
The domain of Machine Learning (ML) encompasses the realm of artificial intelligence
that pertains to the automated identification of patterns within data.
ML can be perceived as an interdisciplinary system, integrating theories from the
branches of statistics, computer science, and cognitive science, with the ultimate
objective of harnessing the intellectual capacity of machines to execute tasks that
emulate human-like capabilities. The underlying premise of machine learning is
predicated upon the human aptitude to acquire knowledge through experiential
exposure, as opposed to reliance on a predetermined series of instructions. Consequently,
programmers endeavour to devise algorithms that, through training on
input data, accrue experience, thereby empowering them to discern patterns and
formulate predictions. The field of machine learning comprises numerous subcategories,
each elucidating distinct learning tasks, encompassing supervised learning,
unsupervised learning, and transfer learning. These learning methodologies can
be distinguished based on their respective objectives, the nature of the input data
upon which they operate, and the learning environments within which they are
deployed.