Course Description
Machine learning has become integral to engineering analytics, significantly improving predictive capabilities and providing valuable insights from complex datasets. In engineering, machine learning models can analyze vast amounts of data from multiple sources to identify patterns and make accurate predictions. These predictions can optimize system performance, predict equipment failures, and improve maintenance schedules. Machine learning techniques transform how engineers approach problem-solving, enabling them to make more informed decisions and implement more effective solutions. One of the critical aspects of this course is the focus on practical examples and hands-on experience with machine learning tools and techniques. Through lectures, case studies, interactive assignments, and projects, students will gain a comprehensive understanding of machine learning applications in engineering analytics. The course will cover fundamental machine learning concepts, such as supervised and unsupervised learning, classification, regression, anomaly detection, and clustering.
Fall 2024
Instructors
Meeting Patterns
Classes Start:
August 19, 2024
Classes End:
December 3, 2024
Location:
01010 Engineering Building I
Class Days:
T H
Class Start Time:
3:00pm
Class End Time:
4:15pm
Class Type:
Lecture
Credits:
3.00
Restrictions:
None
