The intersection of machine learning and engineering represents one of the most dynamic and rapidly growing fields in technology today. As artificial intelligence transforms industries from automotive to healthcare, the demand for professionals who can bridge advanced algorithmic theory with practical engineering applications has never been higher. This comprehensive guide evaluates top-tier machine learning and engineering master’s programs across the United States, providing detailed analysis of costs, curriculum focus, delivery formats, and career outcomes.
The analysis covers everything from 12-month accelerated programs to traditional 2-year degrees, comparing admission requirements, faculty expertise, industry connections, and post-graduation employment outcomes to help you maximize your educational investment.
2027 Machine Learning & Engineering
University of Southern California
Los Angeles, CA - Private 4-year - usc.edu
Master's - MS in Electrical and Computer Engineering (Machine Learning and Data Science)
Campus Based - Visit Website
The Master of Science in Electrical and Computer Engineering with a concentration in Machine Learning and Data Science at USC provides rigorous training in data science, machine learning, and signal processing. The 32-unit curriculum blends theory and practical applications, preparing graduates for high-demand roles like machine learning engineer, data scientist, and software engineer. Students can pursue a thesis or directed research, gaining hands-on experience. Located in Los Angeles, the program offers strong industry connections, with top employers including Amazon, Google, and Meta. The average reported salary is $136,400. International students benefit from OPT STEM extension eligibility. GRE scores are not required for 2027 applications. Scholarship consideration is available for fall applicants. This on-campus program is ideal for those seeking a deep technical foundation in machine learning and data science.
- No entrance exam required
- Test optional
- 32 total credit hours
- Starts spring/fall
- $136,400 median earnings
- No thesis or capstone required
- Financial aid available
- Scholarships available
- Eligible for OPT STEM extension
- Average reported salary $136,400
Master's - MS in Electrical Engineering (Machine Learning and Data Science)
Campus Based - Visit Website
The MS in Electrical Engineering with a concentration in Machine Learning and Data Science at USC Viterbi offers a rigorous, focused curriculum covering theory, methods, and applications of data science, machine learning, and signal processing. Students can complete the program in just 2-3 semesters on campus. Graduates are prepared for top roles like Software Engineer, Electrical Engineer, and Computer Vision Engineer at leading companies such as Apple, Google, and Amazon. This program stands out for its hands-on training and strong industry connections. Whether you're launching your career or advancing in tech, you'll gain the skills to solve real-world problems in AI and data science.
- Complete in Complete your masters in 2-3 semesters!
- Top job titles: Software Engineer, Electrical Engineer, Computer Vision Engineer
- Top employers: Apple, Intel, Google, Boeing, Amazon, Cisco, Qualcomm
- Available exclusively on campus
- Focused training in data science and machine learning
Drexel University
Philadelphia, PA - Private 4-year - drexel.edu
Master's - Master of Science in Machine Learning Engineering
Campus Based - Visit Website
The Master of Science in Machine Learning Engineering at Drexel University equips students with the skills to lead the AI-driven transformation across industries. The 45-credit curriculum covers core machine learning, mathematical theory, applications, signal processing, and electives, with hands-on training using Keras, TensorFlow, and scikit-learn. Students choose between a thesis track, ideal for PhD preparation, or a non-thesis option with flexible electives. The program can be completed full-time in about 18 months or part-time over 3-4 years, making it suitable for working professionals. Faculty are world-leading experts in areas like music understanding, image authentication, and bioinformatics, offering mentorship and research opportunities. Graduates pursue careers in business analytics, healthcare, finance, defense, and top tech companies. The Philadephia campus provides a vibrant urban environment with connections to industry and research. Designed for critical thinking and innovation, this program prepares engineers to address society's biggest challenges through machine learning.
- 18-Month program
- 45 total credit hours
- Full-time and part-time options
- Thesis or capstone option
- Uses Keras, TensorFlow, scikit-learn
- Careers in tech, healthcare, finance, defense
- Graduate advisors guide course selection
- Philadelphia urban campus
- Thesis or non-thesis track available
George Washington University
Washington, DC - Private 4-year - gwu.edu
Master's - Master of Engineering in Artificial Intelligence and Machine Learning
Online Learning - Visit Website
The online Master of Engineering in Artificial Intelligence and Machine Learning at George Washington University equips professionals with advanced skills in machine learning, deep learning, natural language processing, computer vision, and autonomous systems. The 30-credit curriculum blends theoretical foundations with hands-on engineering applications, covering topics from neural networks to trustworthy AI. Graduates pursue roles such as AI engineer, machine learning engineer, and data scientist across technology, finance, healthcare, and transportation. Designed for working professionals, the program offers flexible part-time and full-time options with live evening classes and nine-week sessions. With no application fee, no GRE requirement, and included digital materials, it provides an accessible pathway to leadership in AI.
- No entrance exam required
- Test optional
- $37,500 total program tuition
- $1,250 per credit
- 1-Year program
- Complete in as little as one year
- 30 total credit hours
- 10 total courses
- Full-time and part-time options
- 5 start dates per year
University of Wisconsin-Madison
Madison, WI - Public 4-Year - wisc.edu
Master's - Electrical and Computer Engineering: Machine Learning and Signal Processing MS (Machine Learning and Signal Processing)
Campus Based - Visit Website
The Master of Science in Electrical and Computer Engineering with a named option in Machine Learning and Signal Processing (MLSP) offers an accelerated, course-only curriculum designed for students seeking a direct path into industry. This 16-month, face-to-face program emphasizes quantitative thinking, practical problem-solving, and computer programming, drawing on foundational and cutting-edge methods in machine learning and signal processing. Taught by faculty at the forefront of research, the curriculum covers topics such as matrix methods, neural networks, probability theory, and digital signal processing. Students complete 30 credits, including core courses in machine learning and signal processing, electives, and a hands-on project or internship. The program prepares graduates for roles in data science, machine learning, and signal processing across various industries. With no thesis requirement and a focus on professional development, MLSP students benefit from career fairs, workshops, and networking opportunities. The accelerated timeline allows well-prepared students to finish in as few as 12 months, making it an efficient choice for advancing a technical career.
- Test optional
- 16-Month program
- 30 total credit hours
- 1 start dates per year
- Starts fall
- 3.0 GPA minimum
- 3 letters of recommendation (minimum)
- No thesis or capstone required
- Financial aid available
- Scholarships available
University of Washington-Seattle Campus
Seattle, WA - Public 4-Year - washington.edu
Master's - Master of Science in Artificial Intelligence and Machine Learning for Engineering (Artificial Intelligence and Machine Learning for Engineering, Data Analytics for Systems Operations, AI/ML-Driven Molecular and Process Engineering)
Online & Campus Based - Visit Website
The Master of Science in Artificial Intelligence and Machine Learning for Engineering is a flexible, stackable degree for practicing engineers. It builds on your engineering knowledge, teaching you to apply modern AI and ML methods in fields like manufacturing, chemical processes, and robotics. You start with a required AI/ML certificate, choose a discipline-specific certificate such as Data Analytics for Systems Operations, and finish with an applied capstone. The curriculum covers math, coding, ethics, and domain-specific techniques. With part-time and online options, it accommodates working professionals. No GRE is required, and the deadline for Fall 2027 is June 1st. Graduates pursue roles in AI engineering, data science, and systems optimization. The stacked structure allows you to tailor learning to your field, and the capstone provides hands-on project experience. Developed with support from The Boeing Company, the program ensures industry relevance.
- 5 concentration options
- Test optional
- Apply by 2027-06-01
- Full-time and part-time options
- Starts fall
- 3.0 GPA minimum
- 1 letters of recommendation (minimum)
- Capstone required
- Designed for working engineers
- Stackable degree pathway
University at Buffalo
Buffalo, NY - Public 4-Year - buffalo.edu
Master's - Engineering Science (Artificial Intelligence) MS (data analytics, computational linguistics and information retrieval, machine learning and computer vision)
Campus Based - Visit Website
The Master of Science in Engineering Science with a focus on Artificial Intelligence at the University at Buffalo offers a specialized concentration in Machine Learning and Computer Vision. This multidisciplinary program trains students in machine learning, deep learning algorithms, programming languages, and advanced neural networks for predictive analytics. The curriculum includes foundational AI courses and allows students to tailor their studies with elective concentrations. With 30 credit hours and a time-to-degree of 1.5 to 2 years, the program is offered in-person with both full-time and part-time options. Graduates are prepared for careers as machine learning engineers, AI specialists, and computer vision researchers in industries ranging from technology and healthcare to robotics.
- 4 concentration options
- Complete in 1.5 to 2 Years
- 30 total credit hours
- Full-time and part-time options
- $100 application fee
- Multidisciplinary AI program
- Four elective concentrations available
- In-person instruction (100% on campus)
- Program registered with NYSED
- Foundational AI courses plus specializations
Santa Clara University
Santa Clara, CA - Private 4-year - scu.edu
Master's - Electrical and Computer Engineering M.S. Program (Signal Processing and Machine Learning)
Campus Based - Visit Website
Santa Clara University's Master of Science in Electrical and Computer Engineering offers a focused concentration in Signal Processing and Machine Learning. This 46-unit program covers signal processing algorithms, machine learning techniques, and their real-world applications. Students take a core course and electives in areas like deep learning or communications, with hands-on labs and optional thesis research. Graduates pursue careers as machine learning engineers, data scientists, or signal processing specialists in tech. The program features personalized faculty advising, six focus areas for breadth, and no forced thesis requirement. Located in Silicon Valley, the program provides strong industry connections.
- 6 concentration options
- 46 total credit hours
- 3.0 GPA minimum
- No thesis or capstone required
- Optional thesis up to 9 units
- Advisor-approved program of studies
- Transfer up to 9 quarter units
- Residence: 37 units at SCU
- Six-year completion limit
University of Illinois Chicago
Chicago, IL - Public 4-Year - uic.edu
Master's - Master of Engineering with a focus area in Artificial Intelligence and Machine Learning (Artificial Intelligence, Machine Learning, Neural Networks)
Online Learning - Visit Website
The University of Illinois Chicago's online Master of Engineering with a concentration in Artificial Intelligence and Machine Learning equips engineers with cutting-edge skills in AI, ML, computer vision, and natural language processing. The 36-credit, 9-course curriculum blends technical depth with leadership development and AI ethics. Accelerated 8-week terms allow completion in as few as 12 months, with options for full- or part-time study. Graduates are prepared for roles such as machine learning engineer, data scientist, and AI specialist—fields with strong job growth. The non-thesis, project-based format lets students introduce their own ideas, making it ideal for professionals seeking to advance or pivot into AI careers.
- 2 concentration options
- $896 per credit
- 12-Month program
- 36 total credit hours
- 9 total courses
- Apply by 2026-08-15
- Full-time and part-time options
- No thesis or capstone required
- 100% online program
- Accelerated 8-week terms
Milwaukee School of Engineering
Milwaukee, WI - Private 4-year - msoe.edu
Master's - Master of Science in Machine Learning (Applied Machine Learning, Machine Learning Engineering, Deep Learning)
Online Learning - Visit Website
Milwaukee School of Engineering's online Master of Science in Machine Learning is designed for working professionals ready to advance their technical skills. The program covers deep technical content with immediate industry applications, leveraging MSOE's supercomputer Rosie for hands-on projects. Students can specialize through stackable certificates in Applied Machine Learning or Machine Learning Engineering, each comprising two 4-credit courses. The curriculum emphasizes deploying production-quality ML solutions, ethical considerations, and leading complex data science projects. Graduates emerge as lead architects capable of solving advanced problems in diverse technical fields. Classes meet synchronously two evenings per week, fostering small cohorts and strong faculty support. With 32 credits total and a 3.0 GPA requirement, the program balances rigor with flexibility. No GRE required.
- 2 concentration options
- 32 total credit hours
- 3.0 GPA minimum
- Prerequisite courses required
- Financial aid available
- Online synchronous format
- Access to supercomputer Rosie
- Stackable certificate structure
- Geared for working professionals
- Small class sizes
2027 Lowest Cost Programs
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Choosing the Best Program for You
Program Overview Comparison
This analysis covers seventeen engineering-focused machine learning programs spanning diverse institutions from prestigious Ivy League universities to specialized technical colleges. Programs range from traditional campus-based research degrees to accelerated online professional training:
Premium Research Universities: Drexel, USC, Duke, UCLA, University of Washington, and Dartmouth offer comprehensive research-oriented programs with strong theoretical foundations.
Specialized Engineering Focus: Kettering (automotive AI), Santa Clara (signal processing), University of Wisconsin-Madison (ECE emphasis), and University at Buffalo provide domain-specific engineering applications.
Professional Online Programs: UIC, Tennessee-Knoxville, George Washington, University of Maine, and Milwaukee School of Engineering deliver flexible online education for working professionals.
Industry-Integrated Options: Duke’s product innovation focus, USC’s STEM OPT benefits, and Kettering’s automotive industry connections provide direct career pathways.
Cost Analysis
Most Affordable Public Options
- University of Illinois Chicago: $13,800-$20,700 (in-state), $22,900-$34,400 (out-of-state)
- University of Tennessee-Knoxville: $10,900-$16,300 (in-state), $25,700-$38,500 (out-of-state)
- UCLA: $11,100-$16,700 (in-state), $23,200-$34,800 (out-of-state)
Mid-Range Private Options
- George Washington University: $36,000 total (30 credits × $1,200/credit)
- Milwaukee School of Engineering: $896/credit × 32 credits = $28,672 total
Premium Programs
Most private institutions range $40,000-$70,000+ annually, with programs like Duke, USC, and Dartmouth commanding premium pricing for prestigious credentials.
Program Duration and Format Comparison
Accelerated Options
- UIC: 12-month completion with 8-week terms
- Duke: Flexible 12, 16, or 24-month options
- University of Wisconsin-Madison: 16-month accelerated program
Traditional Timeline
Most programs require 18-24 months (30-45 credits), balancing comprehensive coverage with reasonable completion times.
Delivery Format Diversity
- Fully Online: UIC, Tennessee-Knoxville, George Washington, University of Maine
- Hybrid: Duke, UCLA, University of Washington
- Campus-Based: Drexel, USC, Dartmouth, University of Wisconsin-Madison
Curriculum Specialization Analysis
Engineering Systems Integration
University of Washington and Santa Clara emphasize AI/ML applications within traditional engineering disciplines, bridging theoretical ML with practical engineering solutions.
Industry-Specific Applications
Kettering University uniquely focuses on automotive AI including autonomous driving and sensor fusion, while Duke emphasizes product innovation and commercialization.
Comprehensive Technical Depth
Drexel (45 credits), USC (32 units), and University at Buffalo (30 credits) provide extensive technical coverage spanning multiple ML domains.
Practical Professional Focus
UIC, Milwaukee School of Engineering, and Tennessee-Knoxville emphasize immediate industry application with tools like TensorFlow, PyTorch, and cloud platforms.
Selection Framework: Choosing Your Best Fit
For Research and Academic Excellence
Choose Dartmouth College or USC if you want:
- Prestigious research credentials with thesis requirements
- Strong theoretical foundations in computational science
- PhD pathway preparation
- Access to cutting-edge research facilities
- Premium networking opportunities
For Rapid Professional Advancement
Choose University of Illinois Chicago if you prioritize:
- Accelerated 12-month completion
- Most affordable tuition rates
- 100% online flexibility with 8-week terms
- No thesis requirement for efficiency
- Focus on practical industry skills
For Industry-Specific Specialization
Choose Kettering University if you’re targeting:
- Automotive industry AI applications
- Michigan automotive ecosystem access
- Hands-on engineering focus
- Autonomous vehicle technology specialization
- Direct industry employment pathways
For Flexible Online Learning
Choose University of Tennessee-Knoxville if you need:
- No GRE requirements
- Multiple concentration options
- Live online classes with asynchronous components
- 18-24 month flexible timeline
- Strong cybersecurity integration
For Engineering Systems Integration
Choose University of Washington if you want:
- Hybrid learning format flexibility
- Multiple engineering specialization tracks
- Ethical AI development emphasis
- 3.0 GPA admission accessibility
- Comprehensive engineering applications
Decision Matrix by Priority
Choose Dartmouth/USC if: You can invest in premium education for research credentials and theoretical depth.
Choose UIC if: You need rapid, affordable skill acquisition through online delivery.
Choose Kettering if: You’re targeting automotive industry with specialized technical applications.
Choose Tennessee-Knoxville if: You want accessible, flexible online education with no standardized test barriers.
Choose University of Washington if: You prefer hybrid learning with comprehensive engineering focus.
Critical Considerations
Investment vs. Outcomes Analysis
Premium programs ($40,000-$70,000+) provide prestigious credentials and research opportunities, while affordable options ($11,000-$35,000) offer practical skills for immediate career advancement.
Learning Format Impact
Online programs maximize professional convenience but may lack research opportunities, while campus-based programs provide collaborative learning and faculty mentorship.
Specialization vs. Breadth Trade-offs
Narrow specializations (automotive AI, signal processing) provide deep expertise for specific industries, while broad programs offer flexibility across multiple career paths.
Time Commitment Reality
Accelerated programs (12-16 months) enable rapid career advancement but require intensive study, while traditional timelines (18-24 months) allow deeper learning integration.
The optimal choice depends on career goals, financial constraints, time availability, and industry interests. Working professionals benefit from online accelerated options, while those seeking research careers should consider campus-based programs with thesis requirements. Industry-specific programs provide targeted expertise, while broad technical programs offer maximum career flexibility across engineering disciplines.
Other Concentrations
- Machine Learning for Computer Vision Degree Programs
- Machine Learning for Cybersecurity Programs
- Machine Learning for Data Mining Degree Programs
- Machine Learning for Deep Learning Degree Programs
- Machine Learning for Natural Language Processing Degrees
- Machine Learning Masters Degrees for Signal Processing