Machine Learning Certificate for Engineering Professionals

To be eligible for admission to the Graduate School at Marquette University, applicants must meet the following requirements:

• A bachelor's degree from a regionally accredited institution or international equivalent must be completed prior to starting graduate school.
• A minimum grade point average (GPA) of 3.00 on a 4.00 scale.
• Demonstrated English proficiency for non-U.S. citizens.

Applicants who do not have an engineering degree must complete prerequisite engineering requirements. The list of required prerequisite course(s) is determined during the academic advising process. Students who do not meet the 3.000 requirement, but have completed one year of engineering work experience, are reviewed and considered based upon a letter of recommendation from their supervisor to determine the applicant’s ability to complete advanced course work.

Application Requirements

Read all application instructions prior to beginning an application.

• A completed online application form and fee.
• Transcripts:
  • If coursework was completed within the United States, submit copies of all current and previous college/universities except Marquette¹
  • If coursework was completed outside of the United States, a transcript evaluation is required. A copy of a course-by-course evaluation is approved for the application process.²
• Two letters of recommendation addressing the applicant's suitability for completing graduate-level course work: one from a professor familiar with the student's academic achievement and one from a work supervisor (engineer) or another professor. Waived if the applicant's GPA is 3.000 or above.
• Statement of purpose, describing reasons for pursuing an advanced degree and career goals.
• (For international applicants only) a TOEFL score or other acceptable proof of English proficiency.

^{1Upon admission, final official transcripts from all previously attended colleges/universities, with certified English translations if original language is not English, must be submitted to the Graduate School within the first five weeks of the term of admission or a hold preventing registration for future terms will be placed on the student’s record.} 

^{2Upon admission, an official course-by-course transcript/academic record evaluation must be submitted to the Graduate School within the first five weeks of the term of admission or a hold preventing registration for future terms will be placed on the student’s record.} 

This program has rolling admission, which means you may apply and submit all application materials any time before the following dates:

• Fall term admissions – August 1
• Spring term admissions – December 15 

Applicants who wish to be considered for merit-based financial aid (scholarships) should be aware of the merit-based financial aid deadlines by which all applicant materials must be received by the Graduate School:

• Fall term: February 15
• Spring term: November 15

Dr. Richard J. Povinelli

Dr. Richard J. Povinelli (PhD, Marquette University) is an associate professor in the department of Electrical and Computer Engineering and has over 100 publications in the areas of machine learning and signal processing. He has 25 years of academic and seven years of industrial experience. Dr. Povinelli worked at General Electric Corporate Research and Development as a software engineer and at GE Medical Systems as a project manager.

Dr. Henry Medeiros 

Henry Medeiros (PhD, Purdue University) is an Assistant Professor of Electrical and Computer Engineering, and his research interests include computer vision, robotics, sensor networks, and embedded systems.  Before joining Marquette, he was a Research Scientist at the School of Electrical and Computer Engineering at Purdue University and the Chief Technology Officer of Spensa Technologies, a high-tech start-up company located at the Purdue Research Park.

Dr. Dong Hye Ye

Dong Hye Ye (PhD, University of Pennsylvania) is an Assistant Professor of Electrical and Computer Engineering.  His research interests include machine learning, medical image processing, CT reconstruction, metal artifact reduction, microscopic imaging, automatic target recognition, and unmanned aerial vehicles.