The course introduces machine learning with business
applications. It provides a survey of machine learning techniques,
including traditional statistical methods, resampling techniques,
model selection and regularization, tree-based methods, principal
components analysis, cluster analysis, artificial neural networks,
and deep learning. Students implement machine learning models with
open-source software for data science. They explore data and learn
from data, finding underlying patterns useful for data reduction,
feature analysis, prediction, and classification.
Prerequisites: MSDS 400-DL Math for Data Scientists, MSDS
401-DL Applied Statistics with R, and MSDS 402-DL Introduction to
Data Science.
Starting Fall 2018: ALL sections are taught in
Python
