Statistical and Data Sciences 293 - Modeling for Machine Learning

Modeling for Machine Learning

Albert Y. Kim

M W 10:50 AM - 12:05 PM

Smith College
Ford 240
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)

Permission is required for interchange registration during the add/drop period only.