DATA-55000 Supervised Machine Learning

This course covers methods and theory related to generating predictive models from labeled datasets. Students will get introduced to computational learning theory, study algorithms for generating predictive models, perform feature selection and hyperparameter tuning, and learn how to evaluate model performance. Examples of supervised machine learning techniques covered in the course include naïve Bayes learning, logistic regression, decision tree induction, support vector machines, and deep neural networks. Other, recent developments and state-of-the art methods related to supervised learning may also be covered. Students will be required to write programs that demonstrate machine learning techniques on real-world datasets.

Credits

3

Prerequisite

CPSC 50200 or DATA 50000, and CPSC 50100, DATA 51100, or prior programming experience