MA 415 - Machine Learning
- Credit Hours: 4R-0L-4C
- Term Available: S
- Graduate Studies Eligible: No
- Prerequisites: MA 212* or 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 323 or ME 327 Prerequisite Clarification for MA415: Junior standing and either MA212 or 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.