Course Overview
This course covers the fundamentals of machine learning for data scientists. Students will learn about training data, and how to use a set of data to discover potentially predictive relationships. Topics covered include supervised and unsupervised machine learning, generalized linear models including multivariate linear regression and binary logistic regression, automatic feature selection, bootstrapping, simple reinforcement learning, and decision trees. Applications will be emphasized throughout. A programming language will be used.
DAS 170 and MAT 150; or permission of the instructor.
Program: Data Science
Credit: 3
Faculty
Other Courses
Credits 3
Credits 3
DAS-533G
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Credits 3
Contact
- Data Science
- Hall Memorial Building | N318
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- (202) 651-5000
- (202) 559-5622
