Statistical and Data Sciences 293 - Modeling for Machine Learning
Modeling for Machine Learning
Spring
2024
01
4.00
Albert Y. Kim
M W 10:50 AM - 12:05 PM
Smith College
SDS-293-01-202403
Ford 240
akim04@smith.edu
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). Prerequisites: SDS 291 and MTH 211 (MTH 211 may be concurrent). Enrollment limited to 40.
[CE] SDS 291 & MTH 211 (may be concurrent)