Operational definition of Machine learning is probably best
defined by Tom Mitchell's book Machine Learning,
Machine Learning is the study of computer algorithms that improve automatically through experience. A bit more formally,
A computer program is said to learn from experience E
with respect to some class of tasks T and performance
measure P, if its performance at tasks in T,
as measured by P, improves with experience E.
This implies being able to generate learning curves and supervised learning only.
Clustering being unsupervised learning is ill-defined, because
unsupervised learning initially was referring to Hebbian Learning. Reinforcement learning is really a different beast compare to pervious two and
originates from study of how animals learn, see Watkins story on how he start working on reinforcement learning.
It is true that different branches of Machine learning indeed have different understanding of what constitute by
learning. They don't agree on what is a
pattern either. Instead of saying
its branches differ based on the type of pattern being learnt, if we say
its branches differ based on understanding of what is learning would probably make more justice.