Best Masters Degree Programs for Machine Learning

Drexel University's Master of Science in Machine Learning Engineering is a 45-credit, 18-month program that prepares engineers to lead AI-driven transformation across industries. The curriculum spans core machine learning, mathematical theory, signal processing, and electives, with hands-on training using Keras, TensorFlow, and scikit-learn. Students choose between a thesis track for PhD preparation or a non-thesis option with flexible electives. Full-time study takes about 18 months; part-time extends to 3–4 years, suiting working professionals. Faculty are world-leading researchers in music understanding, image authentication, and bioinformatics, offering mentorship and research opportunities. Graduates pursue careers in business analytics, healthcare, finance, defense, and tech. The Philadelphia urban campus provides industry connections and a vibrant learning environment. Admissions does not specify an entrance exam requirement, focusing instead on academic background and technical aptitude.

• 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

Drexel's Master of Science in Artificial Intelligence and Machine Learning offers two distinct concentrations: Applied for students without a computer science background and Computational for those with a STEM foundation. This 45-credit, STEM-designated program covers mathematical foundations, algorithms, tools, and applications, with coursework in data science, theoretical AI, and domain-specific applications. Students gain hands-on experience through a two-term capstone and an optional graduate co-op. Delivered on campus or online, the quarter system uses 10-week terms for accelerated study. Graduates are prepared for roles like AI/ML engineer, data scientist, and research scientist, supported by faculty active in cutting-edge AI research. The program meets F1 visa STEM requirements and allows integration of stackable certificates. Admission does not list an entrance exam requirement, emphasizing prior coursework and GPA instead.

• 2 concentration options
• Complete in 2-3 years (FT); 2-4 years (PT)
• 45 total credit hours
• Apply by 2026-07-15
• Full-time and part-time options
• Starts fall/winter
• Financial aid available
• Scholarships available
• Hands-on capstone project
• Graduate co-op available

Drexel's online Master of Science in Artificial Intelligence & Machine Learning is tailored for practicing professionals, with three focus areas: data science and analytics, theory of computation and algorithms, and applications of AI/ML. The 46-credit curriculum uses real datasets and state-of-the-art tools, covering deep learning, natural language processing, computer vision, and more. Students can complete the degree in as little as two years with full-time or part-time study, enjoying four start dates per year. A capstone project addresses real-world problems. Entrance exam? No entrance exam is required; the program is test optional. Faculty bring active research in game AI and security. A certificate path supports applicants without strong CS backgrounds. Special tuition rates for military and alumni enhance accessibility. Graduates become data scientists, deep learning engineers, and algorithm developers.

• 3 concentration options
• No entrance exam required
• Test optional
• $1,400 per credit
• 2-Year program
• Complete in in as little as two years
• 46 total credit hours
• Apply by 2026-08-24
• Full-time and part-time options
• 4 start dates per year

Carnegie Mellon University's Master of Science in Machine Learning is a 16-month, on-campus STEM program within the top-ranked School of Computer Science. Students complete nine courses covering machine learning, probabilistic graphical models, optimization, and deep learning, plus three electives for specialization. A practicum—either an internship or research project—provides hands-on industry or academic experience. Admission is highly competitive; accepted students typically hold a 3.9 GPA. No entrance exam is required, and GRE scores are optional. The program offers full-time and part-time options for domestic students, with fall admission only. While no financial aid is available, graduates benefit from CMU's extensive alumni network and career support. Ideal for applicants with strong math and programming backgrounds, this degree prepares leaders in AI and data science.

• No entrance exam required
• Test optional
• 16-Month program
• Complete in can be completed in three semesters
• 9 total courses
• Full-time and part-time options
• 1 start dates per year
• Starts fall
• Prerequisite courses required
• STEM-designated program

Carnegie Mellon University's 5th-Year Master's in Machine Learning offers current CMU undergraduates an accelerated path to a graduate degree in just one additional year. By taking three graduate courses during their bachelor's, students complete the program through full-time, in-person study at the Pittsburgh campus. The curriculum includes an introductory ML course, core electives, statistics, and a summer practicum (internship or research). No GRE or other entrance exam is required, and early admissions deadlines apply. The program is open to any CMU major, with three courses double-counting toward both degrees. Graduates emerge ready for advanced roles in AI, data science, and research, backed by CMU's strong alumni network and career resources.

• 1-Year program
• Apply by 2026-10-27
• Starts fall/spring
• No thesis or capstone required
• No GRE scores required
• Two letters of recommendation
• Full-time in Pittsburgh campus
• Summer practicum (internship/research)
• Open to any CMU major
• Three courses double-count

The University of Texas at Austin offers an online Master of Science in Artificial Intelligence with a specialization in machine learning. This 30-credit, 10-course program covers supervised and unsupervised learning, deep learning, natural language processing, and computer vision. Required coursework includes ethics in AI and case studies in machine learning, blending technical expertise with responsible AI practices. Delivered asynchronously, it allows working professionals to study at their own pace while learning from world-class faculty. Graduates can pursue careers as machine learning engineers, data scientists, or AI researchers. As one of the first fully online AI master's degrees, tuition is notably affordable at $10,000 plus fees, with fall and spring start dates. Entrance exam requirements are not specified.

• 30 total credit hours
• 10 total courses
• Starts fall/spring
• Asynchronous online classes
• Taught by world-class UT Austin faculty
• 100% online program
• One of the first online AI master's programs
• Affordable at $10,000+ fees
• On-demand lectures with weekly release
• Required ethics in AI course

Columbia University's online Master of Science in Computer Science with a Machine Learning concentration, delivered via Columbia Video Network, offers rigorous training in a rapidly expanding field. The curriculum explores core ML techniques and their applications in bioinformatics, finance, and intelligent systems, blending theory with hands-on algorithm development, data analysis, and model building. Geared toward working professionals, the fully online format provides flexibility while maintaining Columbia Engineering's academic rigor. Graduates are prepared for roles as machine learning engineers, data scientists, and AI specialists across healthcare, finance, and technology sectors. The program does not specify a required entrance examination for admission, though graduate-level study typically expects a strong technical background.

• Focus on bioinformatics, finance, intelligent systems
• Ideal for expertise in expanding field
• Online delivery format

UMass Amherst’s M.S. in Electrical and Computer Engineering offers a specialized AI/ML Systems concentration delving into artificial intelligence, machine learning, and data science. Coursework spans introductory to advanced topics, including neural networks, reinforcement learning, image processing, and data analytics. Hands-on labs provide practical tools for building intelligent systems. Students complete five concentration courses alongside core ECE requirements, enabling deep focused study. Faculty research expertise spans machine learning systems, signal processing, and hardware design, offering rich mentorship. Graduates are prepared for research or professional roles in tech, engineering, and applied AI. The program description does not indicate whether a GRE entrance exam is required for admission; applicants should verify current requirements with the department.

• Study foundations of AI, ML, data sciences
• Range from introductory to advanced courses
• Hands-on lab components in courses
• Prepares for research or professional opportunities
• Part of MS in Electrical and Computer Engineering
• Requires 5 concentration courses
• Offered by Electrical & Computer Engineering department
• Featured faculty with AI/ML research

Duke University's Master of Science in Computer Science, with an optional concentration in Artificial Intelligence/Machine Learning, blends theoretical foundations with practical training. The 30-credit program offers course-only or project-based tracks, culminating in an oral exam. Students delve into machine learning algorithms, neural networks, and AI systems, taking the same advanced coursework as PhD candidates. The curriculum emphasizes algorithmic design and computational theory over software engineering. With small cohorts and access to Duke's research community, graduates are poised for doctoral study or roles at top tech firms. Entrance exam requirements are not stated in the available details.

• 2 concentration options
• 30 total credit hours
• 10 total courses
• Project-based or course-only option
• Oral exam as final milestone
• Same courses as PhD students
• Average 760 applications per year
• 4+1 year format for undergrads
• Strong theoretical foundations

The Master of Science in Electrical and Computer Engineering at USC with a Machine Learning and Data Science concentration offers a 32-unit curriculum combining theory, methods, and applications. Students may pursue a thesis or directed research, gaining hands-on expertise in signal processing and data science. Graduates find roles as machine learning engineers, data scientists, and software engineers at Amazon, Google, and Meta, with an average salary of $136,400. International students qualify for OPT STEM extension, and scholarships are available for fall applicants. GRE scores are not required for 2027 applications. This campus-based program emphasizes strong industry connections and prepares students for high-demand AI careers.

• 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

The MS in Electrical Engineering with a Machine Learning and Data Science concentration at USC Viterbi is an accelerated, on-campus program that can be completed in two to three semesters. Its focused curriculum covers data science, machine learning, and signal processing with hands-on training. Graduates are prepared for positions such as software engineer, electrical engineer, and computer vision engineer, with top employers including Apple, Google, Amazon, Intel, and Boeing. The program leverages USC's extensive industry partnerships to solve real-world problems in AI and data science. Entrance exam requirements are not specified, making this an accessible route for career advancement.

• 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

The online Master of Science in Data Science at the University of Rhode Island features a specialized Machine Learning concentration designed for working professionals. The curriculum combines theory with hands-on practice, covering multivariate statistics, machine learning algorithms, data visualization, artificial intelligence, computational statistics, and big data management. Core courses include Machine Learning for Data Science and Advanced Topics in Machine Learning, building proficiency in predictive modeling and automated decision-making. Graduates pursue careers as data scientists, machine learning engineers, or data analysts in technology, finance, healthcare, and other sectors. The program requires 30 credits and an interdisciplinary capstone project. No GRE or entrance exam is required, and the program is test-optional. Its fully online, flexible format supports career advancement. Prerequisites include introductory programming and mathematical skills.

• 3 concentration options
• No entrance exam required
• Test optional
• 30 total credit hours
• Apply by 2026-08-04
• Starts fall
• 2 letters of recommendation (minimum)
• Prerequisite courses required
• No GRE required
• Machine learning focus

Tulane's online M.S. in Computer Science focuses on Artificial Intelligence and Machine Learning, Data Science, and Computational Biology. Students work with internationally recognized researchers on applied topics like NLP, computational geometry, and machine-learning-based therapy discovery. The curriculum emphasizes practical use, such as GPS trajectory analysis, and offers six concentration areas for customizing coursework. Graduates are prepared for specialized roles like AI engineer or data scientist. Cohorts start in fall, spring, and summer, and no relocation is needed. The program does not list an entrance exam requirement.

• 6 concentration options
• Starts fall/spring/summer
• No relocation required
• Internationally recognized researchers
• Customize coursework and areas of emphasis
• Emphasizes advanced technology in practice
• Prepares for highly specialized work

The University of Minnesota's Carlson School offers a 12-month, cohort-based MS in Business Analytics with a dedicated concentration in machine learning and artificial intelligence. Students master data collection, processing, and AI model application to solve real business problems. The curriculum features an AI for Business track, hands-on projects with firms like Amazon and Google, and a capstone through the Carlson Analytics Lab. The program is STEM-designated and test optional, meaning GMAT/GRE scores are not required for admission. Graduates report a three-year average starting salary of $103,051, with a 76% job placement rate. Top employers include major tech and consulting firms. This fast-paced program starts in fall and offers scholarships and financial aid.

• Exams: GMAT, GRE
• Test optional
• 1-Year program
• Complete in 12-month
• 1 start dates per year
• Starts fall
• Cohort-based structure
• 76% job placement rate
• $103,051 mean base starting salary (3-year average)
• 2 letters of recommendation (minimum)