MATH 352 - Machine Learning

Machine Learning(ML) is at the core of advances in science and industry: LLM’s, autonomous vehicles, speech recognition, traffic/weather alerts, recommender systems, vaccine development, etc. Course provides background for ML techniques focusing on supervised learning (linear/logistic regression, lasso, ridge, classification, Naïve Bayes, trees, boosting, bagging, neural networks), and introduces unsupervised learning/cluster analysis.

Prerequisites
MATH 251 Methods of Data Analysis or COMP 218 Data Structures or permission of instructor

Credits 4

Area
Math and Computer Science

Compass Attributes
Natural Science