Machine Learning for Engineering Applications

Catalog description

ECE 571: Machine Learning for Engineering Applications (Cat. I; 3 credits) This is an introductory course for engineering students to gain basic knowledge of machine learning and its applications. This course's objective is to learn machine learning theory and then apply it in engineering practice. A major emphasis of the course is to foster the capability of combining multiple machine learning techniques in complex problem solving, such as the detection of deepfake media. Topics include supervised learning, linear regression, kernel methods, support vector machine, neural networks, unsupervised learning, clustering, principal component analysis, deep learning with convolutional neural networks, and reinforcement learning. Students will develop software to implement machine learning and deep learning algorithms for practical engineering applications. Prerequisites: Basic knowledge of probability and computer programming.