  Machine learning implements backpropagation via abundant training samples. We
demonstrate a multi-stage learning system realized by a promising non-volatile
memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system
consists of unsupervised (clustering) as well as supervised sub-systems, and
generalizes quickly (with few samples). We demonstrate interactions between
physical properties of this device and optimal implementation of
neuroscience-inspired plasticity learning rules, and highlight performance on a
suite of tasks. Our energy analysis confirms the value of the approach, as the
learning budget stays below 20 $\mu J$ even for large tasks used typically in
machine learning.
