EECE 383 - Machine Learning for Engineers

Provides an introduction to machine learning with particular attention on real-world engineering applications. Theoretical foundation and application of supervised learning techniques such as regression and classification as well as unsupervised learning techniques such as clustering and dimensionality reduction. State-of-the-art deep learning algorithms as well as their implementation and use in solving engineering problems. Applications may include object detection and identification in images/videos, pattern recognition in speech/audio, and traffic prediction.

Prerequisites & Notes: EECE 244; MATH 204; MATH 341 or MATH 345.
Credits: 4
Grade Mode: Letter