Machine Learning Engineering MSMLE

Degree Awarded: Master of Science in Machine Learning Engineering (MSMLE)
Minimum Required Credits: 30.0
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

Drexel's MS in Machine Learning Engineering (MSMLE) at Drexel University prepares students to design, implement, and deploy advanced machine learning models that address real-world challenges.

Graduates develop:

  • A deep understanding of core machine learning algorithms, mathematical foundations, and signal processing.
  • Proficiency with industry-standard tools such as TensorFlow, Keras, and scikit-learn for model prototyping and development.
  • Competence in data management, model optimization, and performance evaluation across diverse applications.
  • An interdisciplinary perspective integrating computing, engineering, and applied sciences for problem-solving.

Professional attributes including ethical reasoning, critical thinking, adaptability, and collaborative skills essential for innovative technology development.

The program offers a comprehensive and flexible curriculum that can be tailored to individual goals through consultation with an academic advisor. Students complete 30 credits, combining core coursework with opportunities for specialization in key areas of machine learning engineering.

Students may engage in up to 6 elective credits of faculty-supervised research. Research and project-based learning foster creativity, problem-solving, and innovation through direct engagement with current challenges in the field.

Full-time on-campus students are eligible for the Graduate Co-op Program, a distinctive Drexel experience that integrates academic learning with a 6-month, full-time professional placement. This hands-on opportunity allows students to apply classroom knowledge in practical settings and gain meaningful industry experience before graduation.

Graduates of the MSMLE program are well prepared for roles such as:

  • Machine Learning Engineer, Data Scientist, AI Researcher, and related positions in technology, healthcare, finance, and defense industries.
  • Continued graduate study or doctoral research in machine learning, artificial intelligence, computational engineering, or related fields.
  • Entrepreneurial ventures that apply adaptive AI solutions to emerging societal and industrial needs.

Drexel's MSMLE stands out for its integration of experiential learning, interdisciplinary collaboration, and industry partnership. The program’s customizable curriculum and co-op opportunities bridge academic knowledge with real-world application, empowering students to lead innovations that are both technically robust and socially impactful. Supported by Drexel's strengths in engineering and technology, MSMLE graduates enter the workforce with a competitive edge and a foundation for lifelong growth in the evolving landscape of artificial intelligence.

Additional Information

For more information about this program, please contact the School of Engineering

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.

Degree Requirements

Core/Foundational Courses
ECE 5700Probability and Random Variables3.0
ECE 5701Pattern Recognition3.0
ECE 5704Applied Machine Learning Engineering3.0
ECE 5705Machine Learning and Artificial Intelligence3.0
Subject-Area Focused Courses
Select at least one course from each of the following focus areas:12.0
Analytical
Analytical Methods in Systems
Optimization Methods for Engineering Design
Detection and Estimation Theory
Information Theory and Coding
Applications
Web Security
Bioinformatics
Statistical Analysis of Genomics
Cell and Tissue Image Analysis
Reinforcement Learning
Multimedia Forensics and Security
Signal Processing
Deterministic Signal Processing
Image Processing
Optional Thesis
Select one of the following options:6.0
Non-Thesis
Select any ECE (Electrical and Computer Engineering) 5000-7997 level course 1
Thesis
Master's Thesis
Optional Co-op Experience
Co-op is an option for this degree for full-time on-campus students. Students choosing this option will be required to complete COOP 5000 as preparation for their co-op experience.0.0
Total Credits30.0
1

Elective courses from other subject codes may be taken with department approval.

Program Learning Outcomes

  • Apply knowledge of mathematics, science, and engineering
  • Design and conduct experiments, as well as to analyze and interpret data
  • Design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
  • Function on multidisciplinary teams
  • Identify, formulate, and solve engineering problems
  • Understand professional and ethical responsibility
  • Communicate effectively
  • Understand the impact of engineering solutions in a global, economic, environmental, and societal context
  • Recognize the need for, and an ability to engage in life-long learning
  • Attain knowledge of contemporary issues
  • Use the techniques, skills, and modern engineering tools necessary for engineering practice