The Department of Electrical and Computer Engineering introduces a new certificate program: Machine Learning for Electrical and Computer Engineers.
The ECE Graduate Program has more than twenty faculty who teach courses and perform research in this area. The purpose is to educate electrical and computer engineers in this fundamentally important modern engineering area.
Machine learning techniques have been recently used in all areas of engineering and sciences. Many engineering and science jobs these days require the knowledge of some machine learning techniques, such as deep learning neural network and reinforcement learning methods, and the use of the corresponding software packages.
Graduate students (MS or PhD) from ECE Department or related departments, such as Computer Science, Mechanical Engineering, Biomedical Engineering, do not need to apply separately. Interested students should contact the ECE Graduate Director and Graduate Admin and submit a copy of their transcript and a letter of recommendation. The certificate will be administered by ECE. Documents indicating the completion of the certificate are handled by the Senior Administrator for Degree Certification at SGS.
Applicants should complete the usual application process in the Graduate School application. Applicants need to upload the necessary documents, including transcripts and one letter of recommendation. Applicants should have completed a bachelor’s degree in ECE, CS, or a closely related field.
The following courses related to machine learning are the regular (SGS approved) courses in the Electrical and Computer Engineering Graduate Program:332:509 Convex Optimization
332:510 Optimal Control Systems (covers dynamic programming and calculus of variations)
332:525 Optimum Signal Processing
332:549 Detection and Estimation
332:561 Machine Vision
Several additional Electrical and Computer Engineering courses are also relevant for the machine learning techniques. The complete list of such courses can be found in the SGS catalogue and the ECE Graduate Student Handbook.
332:579 Machine Learning for IoT
332:579 Introduction to Deep Learning
332:579 Cloud Computing
332:579 Computing Principles for Mobile Embedded Systems
The certificate will be awarded to all non-degree and degree ECE M.S. or Ph.D. graduate students who complete four courses (12 credits) in the area certificate program encompassing with a GPA of at least 3.0.
Upon completion of the Certificate Program, the students will be able to use and understand the machine learning techniques to solve practical engineering problems at their workplaces, and to fully understand machine learning software packages used these days in numerous electrical and computer engineering applications. These techniques will be enhanced from the point of views of optimal control systems and optimum digital signal processing, courses regularly taught in the ECE Graduate Program.
Relation to a degree program:
All courses taken within this certificate program are courses regularly taught in the Electrical and Computer Engineering Graduate Program. On-line versions of the regularly taught in class- courses will be developed upon approval of this certificate program.
Mode of Delivery (Classroom Instruction)/Hybrid/Distance Education:
The primary mode of instruction will be long-distance learning. The online courses will be offered in Canvas. The online courses are designed with an instructional designer either from TLT or an in-house resource. The students may also take regularly offered in-class ECE graduate courses to satisfy the certificate requirement. The students will be given an option to take any four courses from aforementioned list of 10 courses, relevant to machine learning techniques in electrical and computer engineering, with at least six courses being offered online. The non-degree students enrolled in this certificate program have an option to complete this certificate fully on-line. In-class courses will also be available to non-degree students. The regular M.S. and Ph.D. students may satisfy the certificate requirement by taking all in-class courses. On-line courses will be also permitted for regular M.S. and Ph.D. students.