Introduction to Machine Learning and Data Sciences

3,6,9 Hours

Discuss machine learning algorithms, including linear models, kernel machines, tree-based algorithms, density estimation, clustering, dimensionality reduction and deep neural networks; foundations of machine learning, including optimization theory, probability theory, advanced linear algebra, statistics; evaluation of machine learning methods; and convolutational neural networks. Engineering Mechanics 397 (Topic: Intro to Machine Learning) and 397 (Topic 6) may not both be counted.