Machine Learning I

Course Number: 46926

The first in a two-part sequence covering statistical machine learning aimed at quantitative finance. This first course covers tools and approaches for prediction, including both regression and classification. The focus is on understanding the foundations of the methods so that they can be both applied and modified. Topics include foundations of supervised learning, the bias-variance tradeoff, model validation and assessment, classification and classification metrics, regularized and nonparametric regression, generalized additive models, and tree-based methods. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student, or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Heinz, Tepper, Computer Science Dept.,or Math Sciences). PhD students with relevant research may be eligible with permission from the instructor.

Concentration: Statistics / Data Science
Semester(s): Mini 2
Required/Elective: Required
Prerequisite(s): 46923