Sparse sampling schemes have the potential to reduce image acquisition time by reconstructing a desired image from a sparse subset of measured pixels. Moreover, dynamic sparse sampling methods have the greatest potential because each new pixel is selected based on information obtained from previous samples. However, existing dynamic sampling methods tend to be computationally expensive and therefore too slow for practical application. In this paper, we present a supervised learning based algorithm for dynamic sampling (SLADS) that uses machine-learning techniques to select the location of each new pixel measurement. SLADS is fast enough to be used in practical imaging applications because each new pixel location is selected using a simple regression algorithm. In addition, SLADS is accurate because the machine learning algorithm is trained using a total reduction in distortion metric which accounts for distortion in a neighborhood of the pixel being sampled. We present results on both computationally-generated synthetic data and experimentallycollected data that demonstrate substantial improvement relative to state-of-the-art static sampling methods.