Introduction to Machine Learning.

Catalog description

This course covers foundational ML topics, including linear and multiple regression, Lasso and Ridge regression, gradient descent, classification methods (kNN, decision trees, random forests, logistic regression), clustering techniques (k-means, k-modes, agglomerative clustering), neural networks (feedforward networks and backpropagation), and applications of large language models (LLMs). Selected engineering machine learning applications will be explored, and the role of responsible AI in contemporary technological advancements will be discussed. Dual-listing: IME 465.