Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.
Online Sync Sessions are an integral part of the online learning experience. Additional information about learning concepts and assignments may be discussed and sync sessions offer valuable opportunities for students to interact with their faculty and peers during the term. We encourage all students to attend live, but if they are unable to, sync sessions will be recorded and posted within Canvas to allow for an asynchronous model of success as well.
MSDS 422-DL : Practical Machine Learning
Description
The course introduces machine learning with business
applications. It provides a survey of statistical and machine
learning algorithms and techniques including the machine learning
framework, regression, classification, regularization and
reduction, tree-based methods, unsupervised learning, and
fully-connected, convolutional, and recurrent neural networks.
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 or MSDS 403--DL Data Science in Practice.
