CSC 546 is a graduate course centered on my research: algorithms for so-called "big data." In this project-oriented course, we read a number of mostly recent papers on algorithms that scale sublinearly or use constant memory. Broadly, topics include sketching and streaming algorithms, dimensionality reduction, fast approximate nearest-neighbor search, the curse of dimensionality and the manifold hypothesis, locality-sensitive hashing, and other topics. Students will write a term paper, possibly in a small group, based on theoretical or empirical research that might lead to a peer-reviewed publication (publication is not a course requirement).