  In [1], we introduced mechanical learning and proposed 2 approaches to
mechanical learning. Here, we follow one such approach to well describe the
objects and the processes of learning. We discuss 2 kinds of patterns:
objective and subjective pattern. Subjective pattern is crucial for learning
machine. We prove that for any objective pattern we can find a proper
subjective pattern based upon least base patterns to express the objective
pattern well. X-form is algebraic expression for subjective pattern. Collection
of X-forms form internal representation space, which is center of learning
machine. We discuss learning by teaching and without teaching. We define data
sufficiency by X-form. We then discussed some learning strategies. We show, in
each strategy, with sufficient data, and with certain capabilities, learning
machine indeed can learn any pattern (universal learning machine). In appendix,
with knowledge of learning machine, we try to view deep learning from a
different angle, i.e. its internal representation space and its learning
dynamics.
