  Machine learning (ML) has found widespread application over a broad range of
important tasks. To enhance ML performance, researchers have investigated
computational architectures whose physical implementations promise compactness,
high-speed execution, physical robustness, and low energy cost. Here, we
experimentally demonstrate an approach that uses the high sensitivity of
reverberant short wavelength waves for physical realization and enhancement of
computational power of a type of ML known as reservoir computing (RC). The
potential computation power of RC systems increases with their effective size.
We here exploit the intrinsic property of short wavelength reverberant wave
sensitivity to perturbations to expand the effective size of the RC system by
means of spatial and spectral perturbations. Working in the microwave regime,
this scheme is tested experimentally on different ML tasks. Our results
indicate the general applicability of reverberant wave-based implementations of
RC and of our effective reservoir size expansion technique
