ENROLL

Anna Rafferty

What does it mean for a machine to learn? Much of modern machine learning focuses on identifying patterns in large datasets and using these patterns to make predictions about the future. Machine learning has impacted a diverse array of applications and fields, from scientific discovery to healthcare to education. In this artificial intelligence-related course, we'll both explore a variety of machine learning algorithms in different application areas, taking both theoretical and practical perspectives, and discuss impacts and ethical implications of machine learning more broadly. Topics may vary, but typically focus on regression and classification algorithms, including neural networks.

Prerequisite: Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)

Rob Thompson

Methods of numerical appoximation and applications to scientific computing and data analysis. Topics will be selected primarily frm numerical linear algebra and optimization. Theory, implementation and application of numerical algorithms will be emphasized.

Prerequisite: Mathematics 232

Katie St. Clair

(Formerly MATH 255) Covers sampling design issues beyond the basic simple random sample: stratification, clustering, domains, and complex designs like two-phase and multistage designs. Inference and estimation techniques for most of these designs will be covered and the idea of sampling weights for a survey will be introduced. We may also cover topics like graphing complex survey data and exploring relationships in complex survey data using regression and chi-square tests.

Prerequisite: Statistics 120 (formerly Mathematics 215) or Statistics 250 (formerly Mathematics 275)