This course will explore the intersection of machine learning and computer systems, focusing specifically on applications of machine learning to computer networks. Topics covered will include applications of machine learning models to security, performance analysis, and prediction problems in computer networks; network measurement and data preparation, feature selection, and feature extraction; design, development, and evaluation of machine learning models for computer networks; and testing and debugging of machine learning models. Students will gain the knowledge of how to use machine learning to help computer systems and computer networks operate better and the practical challenges with deploying machine learning models in practice. Prerequisite: Admission to either the graduate program in Electrical, Computer or Software Engineering or Engineering program advisor's permission.
This course will explore the intersection of machine learning and computer systems, focusing specifically on applications of machine learning to computer networks. Topics covered will include applications of machine learning models to security, performance analysis, and prediction problems in computer networks; network measurement and data preparation, feature selection, and feature extraction; design, development, and evaluation of machine learning models for computer networks; and testing and debugging of machine learning models. Students will gain the knowledge of how to use machine learning to help computer systems and computer networks operate better and the practical challenges with deploying machine learning models in practice. Prerequisite: Admission to either the graduate program in Electrical, Computer or Software Engineering or Engineering program advisor's permission.
