Fundamentals of Machine Learning

3 Credits

Bayesian decision theory and the design of parametric and nonparametric classification and regression: linear, quadratic, nearest-neighbors, neural nets. Boosting, bagging. Credit will not be given for both CSE 326 and CSE 426 . Prerequisites: CSE 017 and ( MATH 205 or MATH 043 ) and ( MATH 231 or ISE 121 or ECO 045 )