10-777 Historical Advances in Machine Learning
12 units, Carnegie Mellon University
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 MLAI 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 andor AI. Since historical is always ambiguous, were going to go with presentedpublished before the instructor was born as a definition pre1988. 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 beforeafter the paper
Prerequisites: 10-315 (min C) or 10-301 (min C) or 10-715 (min C) or 10-701 (min C) or 10-601 (min C)
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