Machine Learning Certificate for Engineering Professionals

At the Opus College of Engineering, we are world-class engineers who will lead bold, innovative change to serve the world in the Jesuit tradition.

By completing the graduate certificate in machine learning for engineering applications, you'll develop the capabilities required to apply current tools and appropriate approaches to solve complex problems in a variety of domains. You’ll gain a greater technical understanding of the elements of machine learning, including algorithms, intelligent systems, neural networks, pattern recognition and deep learning.

Flexible, affordable graduate certificates

We recognize the importance of flexibility to those pursuing graduate education, especially to practicing engineers. That’s why we offer you the opportunity to complete our certificates on a full- or part-time basis. 



Credit Hours


Months to Complete*

online program delivery

      Online and On-Campus  


 *12-18 months to complete, based on a student's individualized plan 


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Topic Areas

The flexible program includes one required course and three elective courses. The required course is designed to explore theoretical foundations of machine learning including: 

  • Computational learning theory
  • Probably Approximately Correct (PAC) of learnability

The remaining credits come from a wide range of elective courses. 

Additional Program Highlights

Flexible course of study

Students can achieve their professional goals with great autonomy by selecting courses that help them achieve their goals.  

Industry focused 

Program was designed in response to shifts in the labor market as the digital economy grows and creates the opportunity to provide the skills that future engineers will need.  

Apply the in-depth coursework directly and immediately to your current position.  

Study with expert faculty 

Students work closely with Marquette faculty, who are experts with extensive industry experience and teach students how to become an "Ignatian Engineer" and develop a skill set with the technical expertise and a reflective mindset to recognize the impact on the world.  Meet the faculty>>

Learn from industry experts

Industry experts will participate in this certificate program through workshops and guest lectures, allowing students to learn and apply their skills based on their expertise.

View the Course Work

Total Credit Hours 12

Required Course:
Machine Learning

Elective Courses

Choose three from the following:

  • Visual Analytics
  • Data Mining
  • Ethical and Social Implications of Data
  • Advanced Machine Learning
  • Data at Scale
  • Introduction to Algorithms
  • Developments in Computer Software
    Fundamentals of Artificial Intelligence
  • Introduction to Intelligent Systems
  • Introduction to Neural Networks and Fuzzy Systems
  • Evolutionary Computation
  • Algorithm Analysis and Applications
  • Artificial Intelligence
  • Pattern Recognition
  • Neural Networks and Neural Computing
  • Advanced Topics in Electrical and Computer Engineering

Other courses as approved by the Certificate Faculty Sponsor, the EECE director of graduate studies (DGS) and the chair of the EECE department.

Learning Outcomes

At the end of the certificate, you will be able to:

  • Analyze complex machine learning problems 
  • Communicate and effectively explain machine learning problems and proposed solutions to a wide range of stakeholders 
  • Integrate advanced machine learning concepts, modern engineering tools, and best practices to recommend technologically appropriate solutions 
  • Reflect on the purposeful and meaningful contribution of each course to achieve the professional growth desired through the certificate    

Apply Your Knowledge

By connecting class work to real-world engineering organizations, industry and academia can come together to embrace new ways of thinking and working together to tackle real problems and solutions.  

Graduates of the program are likely to find positions in a wide range of organizations across industries, including:

  • Healthcare
  • Energy
  • Manufacturing
  • Infrastructure 
  • Finance


Take the next step towards your future


  • Request Information
  • Admission Requirements
  • Application Details
  • Application Deadline
  • Meet the Faculty

Ready to learn more about Marquette's engineering machine learning certificate program? Reach out directly to our program recruiter or fill out the form below (all fields required) and we will respond to you shortly. 

Graduate Program Recruiter

Tim Carter

phone: (414) 288-7139




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

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.

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 PovinelliDr. 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 

Dr. Henry MedeirosHenry 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

Dr. Dong Hye YeDong 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.





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