MACHINE LEARNING

Semester: 
Spring
Year: 
2019
Subject Name: 
Statistical and Data Sciences
Course Number: 
293
Institution: 
Smith College
In the era of “big data,” statistical models are becoming increasingly sophisticated. This course begins with linear regression models and introduces students to a variety of techniques for learning from data, as well as principled methods for assessing and comparing models. Topics include bias-variance trade-off, resampling and cross-validation, linear model selection and regularization, classification and regression trees, bagging, boosting, random forests, support vector machines, generalized additive models, principal component analysis, unsupervised learning and k-means clustering. Emphasis is placed on statistical computing in a high-level language (e.g. R or Python). (E)
Instructor Permission: 
Permission is required for interchange registration during the add/drop period only.
Crosslisted Section ID: 
30779
http://www.smith.edu/catalog/
Schedule #: 
30778-S19

Course Sections

MACHINE LEARNING
Sect # Credits Instructor(s) Instructor Email Meeting Times Location
01 4.0 Albert Kim akim04@smith.edu MW 01:10-02:30 MCCONN 103