MA 415 - Machine Learning
- Credit Hours: 4R-0L-4C
- Term Available: S
- Graduate Studies Eligible: Yes
- Prerequisites: MA 221*, and either MA 223 or MA 381, and either CHE 310 or CSSE 220 or ECE 230 or MA 332 or MA 386 or ME 327 Prerequisite Clarification for MA415: Junior standing and MA221, and either MA223 or MA381, and one of CHE310, CSSE220, ECE230, MA332, MA386 or (ME323 or ME327).
- Corequisites: None
An introduction to machine learning. Topics include: error metrics, accuracy vs interpretability trade-off, feature selection, feature engineering, bias-variance trade-off, under-fitting vs. overfitting, regularization, cross-validation, the bootstrap method, the curse of dimensionality and dimensionality reduction using the singular value decomposition. Both parametric and nonparametric methods are covered including: k-nearest neighbors, linear and logistic regression, decision trees and random forests, and support vector machines. Same as CSSE415.