MS in Machine Learning Engineering
The master’s in machine learning engineering from Drexel Engineering prepares professionals to take on the transformation of science and technology impacted by the field, leading to a successful career in an exciting discipline.
What is a MS in Machine Learning Engineering?
Science and engineering is being transformed through the application of machine learning techniques and principles. A graduate degree program in machine learning engineering allows you to earn the skillsets that help you and your organization best leverage its data, incorporate the coming wave of automation in all its varieties, and understand and explore the potential ways machine learning can improve our lives and environment.
A master’s in machine learning engineering provides knowledge in these three important pillars:
- Fundamentals: Become an expert in the underpinnings of modern machine learning while drawing from an understanding of fundamental principles from various disciplines in order to develop and innovate successful solutions that are best suited to a given problem.
- Implementation: Integrate industry-leading software tools to rapidly prototype machine learning systems. Gain exposure to novel computing architectures of machine learning for implementation of new and advanced outcomes.
- Applications: A graduate program should actively demonstrate how the discipline is put to use in cutting-edge areas where machine learning is being applied in industries ranging from technology, healthcare, bioengineering, smart-cities, the Internet-of-Things, cybersecurity and many others.
A machine learning engineering master’s program should provide an understanding of the forces governing industry, a global viewpoint, and the entrepreneurial, teambuilding and managerial abilities needed to advance careers in business and research or prepare you for entry into a PhD program in a related field.
- Full-time or part-time
- The program will take approximately 18 months to complete on a full-time basis or can be completed on a part time basis in 3-4 years.
Why choose Drexel for your Machine Learning Engineering Degree?
The degree program leverages a long history of producing machine learning experts. Designed with working professionals in mind, graduates go on to obtain positions in diverse fields ranging from business analytics and healthcare to finance and defense, as well as with leading tech companies such as Facebook, Google, Amazon, and Microsoft.
Students in the machine learning degree program gain the ability to implement machine learning systems using cutting-edge software libraries including Keras, TensorFlow, and scikit-learn. You will benefit from classes taught by elite world-leading research experts in areas such as music understanding, image and video authentication, intelligent wireless systems, robotics, cell and tissue image analysis, genomics and bioinformatics. You will emerge prepared to lead and take on the demands of a fast-changing industry, or to continue study in a doctoral program in electrical engineering or related subject.
In the Department of Electrical and Computer Engineering (ECE), and at Drexel, you are encouraged to be innovative and imaginative in identifying the problem and analyzing through critical thinking. The program aims to equip you with the tools for finding sustainable and achievable outcomes to address society’s biggest challenges while also making them relevant to your career goals.
Drexel places a high value on industry connection and teaching. The ECE department’s deep bench of machine learning research expertise allows students to explore related topics at the forefront of the industry.
Kapil R. Dandekar
E. Warren Colehower Chair Professor
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Associate Dean for Enrollment Management and Graduate Education
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Director, Expressive and Creative Interactive Technologies (ExCITe) Center
The city of Philadelphia is our campus – a diverse urban environment with a variety of social, cultural and learning opportunities that will enrich your educational experience. Philadelphia is also a draw for talented instructors and researchers, meaning you will engage with some of the best minds in engineering and other disciplines. Learn more.
Graduate co-op is an optional six-month position for full-time students during Summer and Fall terms. Drexel University co-op provides the opportunity to apply theory and methods learned in classroom and laboratory to a work experience before graduation. The insights help you sharpen career vision and provides vital context for the remainder of the coursework in the program. Participating students benefit from Drexel’s deep ties to industry and the resources from the Steinbright Career Development Center, including programming that enhances professionalism, resume writing, and provides job search resources. For more information, visit the Steinbright Career Development Center.
Curriculum and Requirements
|Aligned Mathematical Theory courses (ECE)
|Applications, Signal Processing (1 course each)
|Mastery (Thesis or Non-Thesis option)
The Master of Science in Machine Learning Engineering plan of study requires a total of 45 credits; 12 credits in core courses; 6 credits of mathematical theory, 3 credits in each applications and signal processing, 9 credits in engineering electives and 6 credits in transformational electives.
Students have a choice of a thesis or a non-thesis option of electives or combined with 9 credits of thesis research, recommended for those interested in doctoral study.
Graduate advisors are available to guide your course selection and scheduling of core and elective courses. Click here to learn more about the Master’s Thesis option.
Dual graduate degrees are also possible. For instance, the degree pairs well with the MS in Computer Engineering, MS in Cybersecurity, or MS in Engineering Management.
Visit the Drexel Catalog for more information or learn more about our admissions requirements.
While not a requirement, all students in the master’s in machine learning engineering program are welcome to engage in research as part of their degree or as extra-curricular participation. Full-time master’s degree candidates or those interested in pursuing a PhD are encouraged to base their master’s thesis on some aspect of faculty research.
Our labs house research conducted by our world-renowned faculty, funded by the U.S. Departments of Defense, Transportation, Health and Human Services, Commerce and Homeland Security as well as with many notable industry partners.
Current research in electrical engineering provides opportunities to participate in research being conducted in machine learning labs such as:
Visit research areas for more about other research activity at the College of Engineering.
Dr. Matthew Stamm's research uses signal processing and machine learning to help determine when images are real, and more importantly, when they are not.
Career Opportunities in Machine Learning Engineering
A machine learning engineering graduate program will prepare you for a career path that could include continuing your education in a PhD program or pursuing advanced technical positions or management in nearly every technology-based industry such as telecommunications companies, high-tech industries, smart manufacturing, electronics manufacturing, information security, automation or robotics.
According to Indeed.com, job postings for Machine Learning Engineers have grown 344% from 2015-2018 and a Machine Learning Engineer position commands an average base salary of $146,085 per year. Overall, employees with graduate degrees can earn up to 28 percent more than bachelor’s degree holders over the course of their career.
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