  $\mathrm{\gamma}$-ray spectroscopy is a quantitative, non-destructive
technique that may be utilized for the identification and quantitative isotopic
estimation of radionuclides. Traditional methods of isotopic determination have
various challenges that contribute to statistical and systematic uncertainties
in the estimated isotopics. Furthermore, these methods typically require
numerous pre-processing steps, and have only been rigorously tested in
laboratory settings with limited shielding. In this work, we examine the
application of a number of machine learning based regression algorithms as
alternatives to conventional approaches for analyzing $\mathrm{\gamma}$-ray
spectroscopy data in the Emergency Response arena. This approach not only
eliminates many steps in the analysis procedure, and therefore offers potential
to reduce this source of systematic uncertainty, but is also shown to offer
comparable performance to conventional approaches in the Emergency Response
Application.
