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Machine Learning for Engineers

E&C-ENG 601

Machine learning is the practice of programming computers to learn and improve prediction through experience and data, and it is becoming pervasive in technology and science. This course will cover the mathematical underpinnings, algorithms, and practices that enable a computer to learn. Topics will include supervised learning, unsupervised learning, evaluation methodologies, and deep learning. The prerequisites of this course include introductory courses in linear algebra (e.g ECE 201 or Math 235), multivariate calculus (e.g., Math 233), and probability (e.g., ECE 214). Knowledge of Python programming is necessary to complete computer assignments and projects. Knowledge of vector spaces concepts, such as norms and vector products (e.g., ECE 565) and matrix algebra is desirable.
Course Details

Spring 2024


Base Cost: $2,100 ($700/credit)
Term Fee: $75
Start date: Feb 01, 2024
End date: May 10, 2024
Last day to add: Feb 14, 2024
Last day to drop: Feb 14, 2024
Last day to withdraw: Apr 04, 2024