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K-means was in optimization in operations research decades before anyone heard of machine learning in computer science. Computer science was not a prerequisite
by graycat 7y ago
K-means was in optimization in operations research decades before anyone heard of machine learning in computer science.
Computer science was not a prerequisite to that work in optimization.
Optimization in operations research and much of applied math more generally are awash in important algorithms without reference to computer science and, really, e.g., the Dantzig simplex algorithm of about 1949, before computer science was a recognized academic field.
Such applied math includes A. Wald's work on sequential testing in statistics, the fast Fourier transform,
Gram-Schmidt orthogonalization, Gauss elimination, Gauss-Siedel iteration, the Hungarian method for maximum matching, Runge-Kutta in ordinary differential equations, and much more.
- scoobyyabbadoo 7y agoOK, but why is it in a book on Linear Algebra today? Look at any introductory Linear Algebra book more than 3 years old and you won't find it in the curriculum
- jazzyjackson 7y agoDo you have a personal vendetta against clustering or something? I don't know too much about linear algebra but I know I had to learn some when my boss wanted me to do unsupervised classification. Maybe the book just considers it a good application of the material. What do old books have to do with what should be included in new books?
- scoobyyabbadoo 7y agoML is hyped enough already and kids already don't know enough fundamentals. They're never going to sit in front of a Linear Algebra class again so the appropriate use of their time is to teach them as much linear algebra as possible and not to turn it into a class on today's hype train