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Title: Using Linear Algebra in Data Science
Speaker: Matt Lamoureux (Travelers)
Time: Wednesday, September 19, 2018 at 5:45 pm
Place: MONT 226Abstract: When building statistical models, we are often interested in dimension reduction: the idea of boiling down many, often correlated, predictors into a few uncorrelated ones. We will discuss one method of doing this, called principal component analysis, which is an application of eigenvalues and matrix decompositions. Some previous exposure to what eigenvalues are would be helpful, although the talk will include a review of the concept.Comments: Free pizza and drinks!
Organizer: Keith Conrad