An introduction to the essential modern supervised and unsupervised statistical learning methods. Topics include review of linear regression, classification, statistical error measurement, flexible regression and classification methods, clustering and dimension reduction. Open only to graduate students in departments other than Statistics and ActSci. Prerequisite: STAT 302 or STAT 305 or STAT 350 or STAT 604 or STAT 605 or ECON 333 or equivalent. Students with credit for STAT 452 may not take this course for further credit.
An introduction to the essential modern supervised and unsupervised statistical learning methods. Topics include review of linear regression, classification, statistical error measurement, flexible regression and classification methods, clustering and dimension reduction. Open only to graduate students in departments other than Statistics and ActSci. Prerequisite: STAT 302 or STAT 305 or STAT 350 or STAT 604 or STAT 605 or ECON 333 or equivalent. Students with credit for STAT 452 may not take this course for further credit.
