Quasars—actively accreting supermassive black holes—are among the most luminous objects in the Universe. Large samples of quasars can be used to study topics including inflationary cosmology, the evolution of black hole growth over the course of cosmic history, and the physics of astrophysical black hole accretion. One of the major challenges for the peta-scale surveys of the future is to classify and estimate the distances to quasars without the need for expensive spectroscopic follow-up. I will present currently used techniques to classify quasars from broadband photometry, focusing on the XDQSO method—a probabilistic method that uses the extreme-deconvolution density estimation technique to handle missing and highly uncertain data—and a critical appraisal of other machine learning methods currently used. Going forward the major challenges will be to (1) incorporate variability and astrometric data into the currently used color selection for optimal quasar selection, (2) separate quasars from galaxies (as opposed to stars) as we go to fainter magnitudes, and (3) strike a balance between data-driven, non-parametric methods—which work well for bright quasars—and template-based techniques—necessary for faint quasars where host-galaxy contamination of the observed flux is significant.