MATH 0218

Statistical Learning

Statistical Learning
This course is an introduction to modern statistical, machine learning, and computational methods to analyze large and complex data sets that arise in a variety of fields, from biology to economics to astrophysics. The theoretical underpinnings of the most important modeling and predictive methods will be covered, including regression, classification, clustering, resampling, and tree-based methods. Student work will involve implementation of these concepts using open-source computational tools. (MATH 0118, or MATH 0216, or BIOL 1230, or ECON 1230, or ENVS 1230, or FMMC 1230, or HARC 1230, or JAPN 1230, or LNGT 1230, or NSCI 1230, or MATH 1230 or SOCI 1230) 3 hrs. lect./disc.
Mathematics & Statistics
Natural Sciences
Requirements Fulfilled:
Equivalent Courses:
STAT 0218 *


Spring 2023

MATH0218A-S23 Lecture (Tang)

Fall 2022

MATH0218A-F22 Lecture (Tang)

Spring 2022

MATH0218A-S22 Lecture (Karpman)

Fall 2021

MATH0218A-F21 Lecture (Lyford)

Spring 2021

MATH0218A-S21 Lecture (Karpman)

Spring 2020

MATH0218A-S20 Lecture (Lyford)

Spring 2017

MATH0218A-S17 Lecture (Kim)