Learning Systems for Engineering Applications

Course Description

The course presents the main methods used in automated (machine) learning for engineering applications. The course discusses representation models for learning, extraction of frequent patterns, classification, clustering and application of these techniques for diverse engineering applications, such as Intranet-of-Things, electronic design automation, and healthcare. The covered topics include an overview of learning systems, learning representations i.e. ontologies, regression models, stochastic models and symbolic models, data preparing techniques, different frequent pattern extraction methods, supervised and unsupervised classification, and basic and advanced clustering algorithms. The course is organized as three modules, each module being centered on a specific theme. Students will learn the characteristics of the enumerated topics, and devise and implement software programs for discussed techniques as part of their project work for the course. Student projects will be assessed using standard benchmarks. 3 credits Grading Letter graded (A, A-, B+, etc.)

Code

ESE 589

Credits

3

Subject

ESE

Programs That Include This Course

• Graduate Certificate Graduate Certificate in Engineering Machine Learning Systems
• Master's Master of Science in Computer Engineering
• Master's Master of Science in Engineering Artificial Intelligence