Historical Advances in Machine Learning

Catalog description

We will read (before class) and discuss (in class) a variety of historically important papers in ML (and to some extent AI). Not all of these were initially published in the ML/AI literature (eg: Bellman in math, VC in probability, bandits in statistics, fuzzy sets in control, optimization work in OR, etc, but now play central roles in ML and/or AI). Since "historical" is always ambiguous, we're going to go with "presented/published before the instructor was born" as a definition (pre-1988). While the content of the paper will be the primary focus, we will also attempt to understand the research context in which the paper was written. For example, what questions were other researchers asking at the time? Was the paper immediately recognized as a breakthrough or did it take a long time? Do we view the contents of the paper today as "obvious in hindsight" or is there still a lot of material in the paper that is nontrivial and even surprising or underappreciated? Who was the author, were they already relatively well known when they wrote the paper, or was it the paper itself that made them famous? What else did these authors work on before/after the paper? Prerequisites: 10-601 Min. grade C or 10-301 Min. grade C or 10-315 Min. grade C or 10-701 Min. grade C or 10-715 Min. grade C