Best Masters Degree Programs for Machine Learning

Drexel University's Master of Science in Machine Learning Engineering is an on-campus program offering a 45-credit curriculum that blends theoretical foundations with practical applications in AI and machine learning. It includes thesis and non-thesis options, taught by research experts using tools like TensorFlow and Keras, with opportunities for hands-on experience and networking in Philadelphia's tech scene. The program does not require an entrance exam, and it is military-friendly, preparing graduates for diverse industries such as technology and healthcare.

• On-campus program.
• Full-time or part-time options.
• 45 credits required.
• Thesis and non-thesis options.
• Cutting-edge software tools.
• Taught by research experts.
• Philadelphia location benefits.
• Research opportunities available.
• Career-focused curriculum.
• Diverse industry applications.

Drexel University's Master of Science in Artificial Intelligence and Machine Learning focuses on applied and computational concentrations, providing a 45-credit curriculum with full-time or part-time options, available on-campus or online. It includes a capstone project for hands-on experience, taught by world-class faculty, and requires a bachelor's in STEM for the computational track. The program does not require an entrance exam, is military-friendly, and offers scholarships, preparing students for real-world AI challenges.

• Top 10 AI program
• Applied and computational tracks
• Full-time or part-time
• On campus or online
• 45 credits required
• Capstone project included
• STEM designated
• Quarter system curriculum
• Scholarships available
• Hands-on experience

Drexel University's online Master of Science in Artificial Intelligence & Machine Learning offers concentrations in Data Science and Analytics, Theory of Computation and Algorithms, and Applications of AI/ML. This 45-quarter credit program requires a strong computer science background, a 3.0 GPA for admission, and includes a capstone project with real datasets. GRE scores are recommended for some applicants, but not required; tuition is $1438 per credit with financial aid available, and it is military-friendly, focusing on ethical AI and career preparation.

• 45-quarter credit program.
• Concentrations in Data Science, Algorithms, AI/ML.
• Online format for flexibility.
• $1438 per credit tuition.
• Financial aid available.
• Requires strong computer science background.
• Capstone project included.
• Full- or part-time options.
• 10-week quarter terms.
• Interdisciplinary curriculum.

Carnegie Mellon University's Master of Science in Machine Learning is a prestigious 16-month campus program requiring 6 core courses, 3 electives, and a practicum. It emphasizes strong backgrounds in computer science, mathematics, and programming, with GRE scores optional for 2025 applicants. As a STEM-designated program, it enhances post-graduation opportunities in AI and data science, but does not provide financial support. Ideal for students seeking advanced knowledge without an entrance exam requirement.

• 16-month program duration
• Campus-based learning
• 6 core courses required
• 3 elective courses
• Practicum component
• Strong math background needed
• Programming skills essential
• No financial support
• GRE optional for 2025
• STEM-designated program

The Master's in Machine Learning - Applied Study at Carnegie Mellon University is a 16-month campus-based program focused on industry careers, featuring a mandatory internship and professional development. It requires 6 core courses and 3 electives, with prerequisites in computer science, mathematics, and programming skills in Python or Java. This STEM-designated program does not require an entrance exam and offers no financial support, targeting practical machine learning skills for competitive admissions.

• 16-month program duration.
• Campus-based learning.
• STEM-designated program.
• Mandatory internship included.
• 6 core courses required.
• 3 elective courses.
• Professional development activities.
• Strong CS and math prerequisites.
• Programming skills in Python/Java.
• No financial support provided.

Carnegie Mellon University's 5th-Year Master's in Machine Learning is a one-year campus program for CMU undergraduates, allowing double-counting of up to three courses. It includes core and elective courses, a practicum, and requires no GRE scores. Admission is competitive, necessitating a CMU bachelor's degree, specific course grades of B or better, and submission of documents like a statement of purpose and letters of recommendation, with no online or part-time options.

• One-year program duration.
• Double-count up to three courses.
• Full-time study required.
• Campus-based program.
• Practicum included.
• Competitive admissions.
• No GRE scores required.
• Two letters of recommendation.
• Statement of purpose needed.
• Mid-semester grades submission.

Johns Hopkins University offers an online Master of Science in Artificial Intelligence, ideal for professionals seeking to excel in AI. This program emphasizes machine learning, natural language processing, and robotics, blending theory with hands-on application. Developed by leading researchers, it equips students to drive innovation in AI technologies. The flexible format allows for tailored studies, with year-round admissions. A bachelor's degree is required for entry; no entrance exam is specified, but check official sources for updates. Many students benefit from employer tuition support, enhancing accessibility.

• Online program format
• Covers AI, machine learning
• Flexible course options
• Year-round applications
• Employer tuition contributions
• Bachelor's degree required
• Practical and theoretical focus
• Top researchers as instructors
• Innovation in AI technologies
• Tailored study options

Northeastern University's Master of Science in Artificial Intelligence with a Machine Learning concentration provides a rigorous 32-credit curriculum on campus in Boston. It emphasizes practical skills in data science, deep learning, and robotics, supported by top research facilities and industry partnerships. The program includes co-op opportunities for real-world experience and requires an entrance exam, such as the GRE, for admission, focusing on academic excellence and innovation potential.

• 32 credit hours required
• Concentration in Machine Learning
• Campus-based program
• Located in Boston, MA
• Financial aid available
• Co-op opportunities
• Cutting-edge research facilities
• Industry connections
• Comprehensive AI curriculum
• Practical applications focus

The M.S. Concentration in AI/ML Systems at the University of Massachusetts-Amherst provides an in-depth focus on artificial intelligence and machine learning, integrating theoretical principles with practical lab work. Students complete five specialized courses covering areas such as image processing and neural networks, preparing them for research or industry roles. This campus-based program requires meeting the Electrical and Computer Engineering MS degree criteria for admission, which may include entrance exams like the GRE, though specifics should be verified with the institution.

• Focus on AI/ML Systems.
• Campus-based program.
• Five specialized courses required.
• Lab components included.
• Prepares for research or professional roles.
• Admission based on MS degree criteria.
• Covers image processing to neural networks.
• Blends theory with practical experience.
• For students from diverse backgrounds.
• Focus on both hardware and software.

The University of Southern California's MS in Electrical and Computer Engineering with a concentration in Machine Learning and Data Science provides intensive training in data science theory and applications, focusing on extracting insights from large datasets to advance technology and society. This 32-unit campus-based program is eligible for the OPT STEM extension, appealing to international students, and requires a bachelor's degree in engineering or a related field with coursework in calculus, differential equations, linear algebra, and programming. It emphasizes a blend of theoretical and practical learning, preparing graduates for high-demand roles in the field. An entrance exam is required for this master's program.

• 32 units of coursework.
• OPT STEM extension eligible.
• Campus-based program.
• Focus on Machine Learning and Data Science.
• Bachelor’s Degree required.
• Engineering or related fields preferred.
• Coursework in calculus, differential equations, linear algebra, and programming needed.
• Rigorous training in data science.
• Theoretical and practical learning blend.
• Prepares for high demand in data science fields.

The University of Southern California's MS in Electrical Engineering with a concentration in Machine Learning and Data Science offers a rigorous on-campus curriculum that can be completed in 2-3 semesters, providing fast-track expertise in machine learning and data science applications. Designed for students at various career stages, it prepares graduates for high-demand technology and engineering roles. A strong academic background in engineering or related fields is required for admission, with details on costs and applications available online. An entrance exam is required for this master's program.

• 2-3 semesters completion.
• On-campus program.
• Focus on machine learning.
• Data science concentration.
• Rigorous training in theory.

Columbia University's online MS in Computer Science with a Machine Learning concentration provides advanced training in AI, algorithms, and statistical modeling, tailored for professionals in tech industries. The program emphasizes practical applications in fields like finance and bioinformatics, delivered through a flexible online format. Admission requires a bachelor's degree in a related field, GRE scores, and programming proficiency. Graduates are prepared for high-demand roles, with a curriculum that integrates theory and hands-on projects.

• Online MS program.
• Focus on Machine Learning.
• Flexible for professionals.
• Covers AI and data.
• GRE scores required.
• Programming proficiency needed.
• Blends theory and practice.
• Prepares for tech roles.

Duke University's Master of Engineering in Artificial Intelligence for Product Innovation, with a concentration in Machine Learning, is an online program designed for professionals seeking to integrate AI and ML into product development. It offers flexible 12, 16, or 24-month options, emphasizing hands-on projects and industry collaborations for real-world applications. Admissions require a background in engineering, science, or technology, and an entrance exam such as the GRE is typically required, though specific details should be verified on the university's website. This program prepares graduates for leadership roles in tech and healthcare sectors.

• Concentration in Machine Learning.
• Flexible 12, 16, 24-month options.
• Hands-on real-world projects.
• Industry collaborations included.
• Background in STEM required.

The University of Florida's Master of Science in Artificial Intelligence Systems is an interdisciplinary program that integrates computer science, mathematics, engineering, and ethics through a 30-credit curriculum. It includes six core courses, three electives, and a capstone project, utilizing the HiPerGator supercomputer for hands-on experience. This campus-based program prepares graduates for roles in IT, healthcare, and finance, emphasizing practical skills and ethical AI development. Admission requires a bachelor's degree in a related field, and an entrance exam is not mentioned as required.

• 30 credit hours
• Interdisciplinary curriculum
• HiPerGator supercomputer access
• Capstone project included
• Campus-based program
• Focus on ethics in AI
• Prepares for IT, healthcare, finance careers
• Bachelor's degree required for admission

Harvard University's Master's in Data Science with a Machine Learning concentration focuses on advanced statistical modeling, big data analytics, and machine learning techniques. This campus-based program requires 12 courses over at least three semesters, offering flexibility for thesis research and practical skills like data visualization and ethical data management. Ideal for students with backgrounds in calculus, linear algebra, and programming, it prepares graduates for diverse careers in data science. Financial aid includes loans, scholarships, and teaching fellowships. An entrance exam such as the GRE may be required for admission.

• 12 courses required
• 3 semesters minimum
• Machine Learning concentration
• Campus-based program
• No formal prerequisites
• Background in calculus needed
• Programming fluency required
• Interdisciplinary field
• Financial aid available
• Teaching fellowships offered