CMU 10-725 Convex Optimization
Carnegie Mellon University,, Fall 2018 , Prof. Ryan Tibshirani
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Updated On 02 Feb, 19
Nearly every problem in machine learning and computational statistics can be formulated
in terms of the optimization of some function, possibly under some set of constraints. As
we obviously cannot solve every problem in machine learning, this means that we cannot
generically solve every optimization problem (at least not efficiently). Fortunately, many
problems of interest in machine learning can be posed as optimization tasks that have
special propertiessuch as convexity, smoothness, sparsity, separability, etc.permitting
standardized, efficient solution techniques.
4.1 ( 11 )
Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
March 29, 2019
Great course. Thank you very much.