Master’s in Data Science for MIT MicroMaster's Graduates
Course Descriptions and Schedule
Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.
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 Modelers, and MSDS
401-DL Applied Statistics with R, and MSDS 402-DL Introduction to
Data Science or MSDS 403--DL Data Science in Practice.
Starting in fall 2023, MSDS 400-DL and MSDS 401-DL will be
the only prerequisites.
