This paper presents a machine-learning classifier where computations are performed in a standard 6T SRAM array, which stores the machine-learning model. Peripheral circuits implement mixed-signal weak classifiers via columns of the SRAM, and a training algorithm enables a strong classifier through boosting and also overcomes circuit nonidealities, by combining multiple columns. A prototype 128 x 128 SRAM array, implemented in a 130-nm CMOS process, demonstrates ten-way classification of MNIST images (using image-pixel features downsampled from 28 x 28 = 784 to 9 x 9 = 81, which yields a baseline accuracy of 90%). In SRAM mode (bit-cell read/write), the prototype operates up to 300 MHz, and in classify mode, it operates at 50 MHz, generating a classification every cycle. With accuracy equivalent to a discrete SRAM/digital-MAC system, the system achieves ten-way classification at an energy of 630 pJ per decision, 113x lower than a discrete system with standard training algorithm and 13x lower than a discrete system with the proposed training algorithm.