Special Topics: Conceptual Foundations of Statistical Learning

9 units · Undergraduate

Intermittent: 9 units This class is an introduction to the foundations of statistical learning theory, and its uses in designing and analyzing machine-learning systems. Statistical learning theory studies how to fit predictive models to training data, usually by solving an optimization problem, in such a way that the model will predict well, on average, on new data. The course will focus on the key concepts and theoretical tools, at a level accessible to students who have taken 36-401 and its pre-requisites. The course will also illustrate those concepts and tools by applying them to carefully selected kinds of machine learning systems (such as kernel machines). Students wanting exposure to a broad range of algorithms and applications would be better served by 36-462 /662 ("Data Mining"). This class is for those who want a deeper understanding of the principles underlying all machine learning methods. Prerequisite: 36-401 Min. grade C