Machine Learning Engineering MSMLE
Major: Machine Learning Engineering
Degree Awarded: Master of Science in Machine Learning Engineering (MSMLE)
Calendar Type: Quarter
Minimum Required Credits: 45.0
Co-op Option: Available for full-time, on-campus master's-level students
Classification of Instructional Programs (CIP) code: 14.0903
Standard Occupational Classification (SOC) code: 15-1132
About the Program
The Master of Science in Machine Learning Engineering (MSMLE) at Drexel University is a comprehensive program designed to equip students with the skills and knowledge necessary to excel in the rapidly evolving field of machine learning. The curriculum emphasizes core machine learning topics, mathematical theory, and signal processing, providing a solid foundation for designing, implementing, and applying machine learning algorithms to real-world problems. Students gain expertise in leveraging industry-leading software tools such as TensorFlow, Keras, and scikit-learn for rapid prototyping and development of advanced machine learning systems. The program also introduces novel computing architectures and cutting-edge applications across industries like healthcare, bioengineering, smart cities, cybersecurity, and more.
The degree requires a minimum of 45.0 approved credits. The curriculum is customizable, with students developing their plan of study in consultation with their academic advisor.
Students graduating from Drexel’s MSMLE program are prepared for a career as a machine learning expert engineer or similar positions. The degree fosters critical thinking and innovation by integrating principles from various disciplines essential for creating tailored solutions to complex challenges. Students develop both individual and team-based implementation skills through practical coursework. It is ideal for individuals seeking to deepen their understanding of machine learning principles or pursue entrepreneurial ambitions while addressing societal challenges through sustainable solutions.
Students also have the option to engage in research under the supervision of an ECE faculty member. A total of 9.0 credits of research-oriented coursework may be counted towards the minimum credit hour requirement of the degree program. These credits are considered general electives.
The MS program is designed so that a student may complete the degree requirements in less than 2 years of full-time study or 2-3 years of part-time study.
Full-time on-campus students who start in a fall term are eligible to take part in the Graduate Co-op Program, which combines classroom coursework with a 6-month, full-time work experience. For more information, visit the Steinbright Career Development Center's website or visit the Drexel Engineering Graduate Co-op webpage.
Additional Information
For more information visit the MS in Machine Learning Engineering program, or the Department of Electrical and Computer Engineering website.
Admission Requirements
Applicants must satisfy general requirements for graduate admission including a minimum 3.0 GPA (on a 4.0 scale) for the last two years of undergraduate studies, as well as for any subsequent graduate work. Applicants will be required to hold a bachelor's in electrical engineering, computer engineering, or computer science. Applicants with a bachelor’s degree in an aligned area (e.g. statistics, neuroscience, etc.) in addition to an appropriate technical background will also be considered during the admissions process. Prior coursework or experience with signal processing, probability, statistics, and programming languages is preferred.
The GRE general test is optional for all applicants. TOEFL, IELTS, PTE, or Duolingo is required if the language of instruction of your previous degree was not English.
For additional information on how to apply and deadlines, visit the College of Engineering's Admission Guidelines or Drexel's admissions page for Machine Learning Engineering.
Degree Requirements
| Core Courses | ||
| ECE 610 | Machine Learning & Artificial Intelligence | 3.0 |
| ECE 612 | Applied Machine Learning Engineering | 3.0 |
| ECE 687 | Pattern Recognition | 3.0 |
| ECES 521 | Probability & Random Variables | 3.0 |
| Aligned Mathematical Theory | ||
| Select two from the list below: | 6.0 | |
| Analytical Methods in Systems | ||
| Random Process & Spectral Analysis | ||
| Detection & Estimation Theory | ||
| Optimization Methods for Engineering Design | ||
| Information Theory and Coding | ||
| Applied Probability and Statistics I | ||
| Numerical Analysis II | ||
| Applied Engr Analy Methods I | ||
| Applied Engr Analy Methods II | ||
| Applied Engr Analy Methods III | ||
Any MATH (Mathematics) 500-900 level course | ||
| Applications | ||
| Select one from the list below: | 3.0 | |
| Neuromorphic Computing | ||
| Reinforcement Learning | ||
| Cell & Tissue Image Analysis | ||
| Multimedia Forensics and Security | ||
| Bioinformatics | ||
| Statistical Analysis of Genomics | ||
| Signal Processing | ||
| Select one from the list below: | 3.0 | |
| Fundamentals of Deterministic Digital Signal Processing | ||
| Fundamentals of Computer Vision | ||
| Fundamentals of Image Processing | ||
| Elective Courses | ||
| Select 15.0 credits at the 500-900 level from the subject areas listed below: | 15.0 | |
Any AE (Architectural Engineering) | ||
Any BIO (Bioscience & Biotechnology) | ||
Any BMES (Biomedical Engineering & Sci) | ||
Any CHE (Chemical Engineering) | ||
Any CHEM (Chemistry) | ||
Any CIVE (Civil Engineering) | ||
Any CMGT (Construction Management) | ||
Any CS (Computer Science) | ||
Any ECE (Electrical Engineering) | ||
Any ECEC (Elec & Comp Engr-Computers) | ||
Any ECEE (Elec & Computer Engr-Electroph) | ||
Any ECEP (Elec & Computer Engr-Power Eng) | ||
Any ECES (Elec & Computer Engr-Systems) | ||
Any ECET (Telecommunications) | ||
Any EGMT (Engineering Management) | ||
Any ENGR (Engineering) | ||
Any ENTP (Entrepreneurship) | ||
Any ENVE (Environmental Engineering) | ||
Any ET (Engineering Technology) | ||
Any MATE (Materials Engineering) | ||
Any MATH (Mathematics) | ||
Any (MEM) Mechanical Engr & Mechanics | ||
Any MGMT (Management) | ||
Any MIS (Management Information Systems) | ||
Any OPR (Operations Research) | ||
Any PHYS (Physics) | ||
Any PROJ (Project Management) | ||
Any SYSE (Systems Engineering) | ||
| Mastery (Thesis and Non-Thesis Option) * | 6.0 | |
| Master's Thesis | ||
See footnote for more information about thesis and non-thesis options. | ||
| Optional Co-op Experience ** | 0-1 | |
| Career Management and Professional Development for Master's Degree Students | ||
| Total Credits | 45.0-46.0 | |
- *
Thesis Option: A minimum of two terms (3.0 credits per term; 6.0 total credits) of laboratory-based research that leads to a publicly defended MS thesis. Students will be advised by a faculty member, and when applicable, a representative of industry or government sponsor.
Non-thesis Option: In lieu of research and a thesis, students will complete 6.0 credits of coursework from the Aligned Mathematical Theory, Applications, or Signal Processing areas.
- **
Co-op is an option for this degree for full-time on-campus students. To prepare for the 6-month co-op experience, students will complete: COOP 500. The total credits required for this degree with the co-op experience is 46.0
Students not participating in the co-op experience will need 45.0 credits to graduate.
Sample Plan of Study
Thesis Option
| First Year | |||||||
|---|---|---|---|---|---|---|---|
| Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
| ECE 687 | 3.0 | ECE 612 | 3.0 | ECE 610 | 3.0 | VACATION | |
| ECES 521 | 3.0 | Aligned Mathematical Theory | 6.0 | Applications course | 3.0 | ||
| Signal Processing course | 3.0 | Engineering elective | 3.0 | ||||
| 9 | 9 | 9 | 0 | ||||
| Second Year | |||||||
| Fall | Credits | Winter | Credits | ||||
| ECE 898 | 3.0 | ECE 898 | 3.0 | ||||
| Engineering Electives | 6.0 | Engineering Electives | 6.0 | ||||
| 9 | 9 | ||||||
| Total Credits 45 | |||||||
Non-Thesis Option
| First Year | |||||||
|---|---|---|---|---|---|---|---|
| Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
| ECE 687 | 3.0 | ECE 612 | 3.0 | ECE 610 | 3.0 | VACATION | |
| ECES 521 | 3.0 | Aligned Mathematical Theory | 6.0 | Applications course | 3.0 | ||
| Signal Processing course | 3.0 | Engineering Elective | 3.0 | ||||
| 9 | 9 | 9 | 0 | ||||
| Second Year | |||||||
| Fall | Credits | Winter | Credits | ||||
| Aligned Mathematical Theory, Applications, or Signal Processing | 3.0 | Aligned Mathematical Theory, Applications, or Signal Processing | 3.0 | ||||
| Engineering Electives | 6.0 | Engineering Electives | 6.0 | ||||
| 9 | 9 | ||||||
| Total Credits 45 | |||||||
Full Time With CO-OP
| First Year | |||||||
|---|---|---|---|---|---|---|---|
| Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
| COOP 500 | 1.0 | Signal Processing or Aligned Mathematical Theory Courses | 6.0 | ECE 610 | 3.0 | ECE 612 | 3.0 |
| ECE 687 | 3.0 | Engineering Elective | 3.0 | Applications Course | 3.0 | Aligned Mathematical Theory, Applications, or Signal Processing Course | 3.0 |
| ECES 521 | 3.0 | Engineering Elective | 3.0 | Engineering Elective | 3.0 | ||
| Signal Processing or Aligned Mathematical Theory Course | 3.0 | ||||||
| 10 | 9 | 9 | 9 | ||||
| Second Year | |||||||
| Fall | Credits | Winter | Credits | Spring | Credits | ||
| COOP EXPERIENCE | COOP EXPERIENCE | Aligned Mathematical Theory, Applications, or Signal Processing Course | 3.0 | ||||
| Engineering Electives | 6.0 | ||||||
| 0 | 0 | 9 | |||||
| Total Credits 46 | |||||||