Electrical & Computer Engin 601 - Machine Learning for Engineers

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.