Machine Learning For Engineering Applications Certificate
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.
The Dean of the Opus College of Engineering is offering tuition credits to support engineers completing certificates. Students who complete six credits of a certificate will be eligible for a two-credit tuition discount on the remaining six credits. For more information, please speak with our admissions specialist.
Online and On-Campus
*12-18 months to complete, based on a student's individualized plan
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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.
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.
Total Credit Hours 12
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.
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
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:
Take the next step towards your future
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
phone: (414) 288-7139
Email the Graduate School
This program has rolling admission, which means you may apply any time before the following dates:
- Fall term admissions- August 1 (June 1 for international applicants)
- Spring term admissions- December 15 (October 15 for international applicants)
Priority consideration is given to applicants who apply by February 15.
These are the dates your application must be complete and all required documentation must be received in the Graduate School by these dates.
Merit-Based Financial Aid Deadlines
Applicants who wish to be considered for merit-based financial aid (graduate assistantships/fellowships) should be aware of the following merit-based financial aid deadlines by which all applicant materials must be received by the Graduate School:
- Fall (August) Term: February 15
- Spring (January) Term: November 15
- Summer (May) Term: April 15
Read all application instructions prior to beginning an application.
- A completed application form and fee online.
- Copies of all college/university transcripts except those from Marquette.1
- 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.
Upon 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.
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.
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.
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.
Why Choose Marquette?
College of Engineering Department
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