These days, established computer vision professors are given to complaining, with varying degrees of seriousness, that current Ph.D. students do not know any work in the field that pre-dates the “deep learning revolution” of 2012. However, while wholesale amnesia is unquestionably dangerous for the field, from a pragmatic point of view, even the “old guard” concedes that it is no longer necessary to teach historic work that was truly an intellectual dead end. This short course is an attempt to grapple with the question of what “classical” computer vision techniques should be considered a “must know” for researchers entering the field today, and how past trends and approaches should inform the field as it looks poised to enter a challenging phase—continuing its spurt of rapid growth even while the initial momentum from the “deep learning revolution” begins to fade and negative societal impacts of some maturing technologies come into view.