A master’s in machine learning can help students build advanced skills in model development, deep learning, natural language processing, computer vision, recommender systems, AI engineering, MLOps, and applied research.
These programs are designed for students who want to move beyond general programming or analytics into more technical roles involving predictive models, large-scale AI systems, model deployment, and responsible AI.
The best machine learning master’s degree depends on your background and career goal. Some students need a research-focused program with a thesis and faculty mentorship. Others may be better served by a professional, online, or part-time program focused on applied ML, cloud deployment, and production AI systems.
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What Is a Master’s in Machine Learning?
A master’s in machine learning is a graduate degree focused on the theory, design, evaluation, and deployment of algorithms that allow computer systems to learn from data. Programs usually combine mathematics, statistics, computer science, data engineering, and applied AI.
Students may study topics such as supervised learning, unsupervised learning, reinforcement learning, deep learning, natural language processing, computer vision, optimization, model evaluation, data pipelines, and MLOps.
A machine learning master’s degree is usually more specialized than a general master’s in computer science and more technical than many analytics-focused data science programs. It may be a strong fit for students who want to become machine learning engineers, AI engineers, applied scientists, research engineers, data scientists, or MLOps engineers.
Machine Learning vs. Artificial Intelligence vs. Data Science vs. Computer Science
Students comparing graduate programs often confuse machine learning, artificial intelligence, data science, and computer science. These fields overlap, but they are not identical.
| Degree | Best for | Main focus | Common careers |
| MS in Machine Learning | Students focused on ML algorithms, model development, applied research, and ML engineering | Statistical learning, deep learning, optimization, model evaluation, deployment | ML engineer, applied scientist, AI engineer, ML researcher |
| MS in Artificial Intelligence | Students who want broader AI systems training | ML, NLP, computer vision, robotics, reasoning, responsible AI | AI engineer, ML engineer, AI product specialist, applied scientist |
| MS in Data Science | Students focused on extracting insights from data | Statistics, machine learning, data engineering, visualization, experimentation | Data scientist, analytics engineer, ML analyst |
| MS in Computer Science with ML track | Students who want broad CS depth plus ML specialization | Algorithms, systems, databases, AI/ML, software engineering | Software engineer, ML engineer, systems engineer, research engineer |
| MS in Statistics or Applied Math | Students focused on theory-heavy modeling and quantitative research | Probability, inference, statistical learning, optimization | Data scientist, quantitative researcher, ML researcher |
| MS in Software Engineering | Students focused on building production systems | Software architecture, testing, DevOps, cloud systems, product delivery | Software engineer, MLOps engineer, platform engineer |
A dedicated MS in machine learning is usually narrower and more technical than a general AI or data science degree.
A master’s in artificial intelligence may cover broader AI systems, including robotics, reasoning, NLP, and responsible AI.
A data science master’s degree often places more emphasis on analytics, statistics, business decision-making, and data visualization.
A computer science master’s degree with an ML track may offer broader software, systems, and algorithms depth.
Related Resources
Tuition rates
Campus-based Master’s programs in Machine Learning provide advanced, in-person training for specialized roles, summarized below across 11 options.
- Highest Per-Credit Rate: $8,173.
- Average Total Cost: $77,118.
- Lowest Per-Credit Rate: $412.27.
Online Master’s degrees in Machine Learning offer flexible, high-level training to boost your career, highlighted by these stats from 6 programs.
- Average Total Cost: $51,316.
- Lowest Per-Credit Rate: $675.
- Highest Per-Credit Rate: $2,700.
Best Campus Master’s in Machine Learning Programs for 2026
Carnegie Mellon University
Pittsburgh, Pennsylvania
Program: Master of Science in Machine Learning
ARTiBA accreditation: Yes
Delivery method: Campus
Total tuition: $31,500
2026 Cost per credit: $1,050
Credits: 30
Learn more: Program detailsUniversity of North Texas
Denton, TexasProgram: Master of Science in Artificial Intelligence (Machine Learning Concentration)
ARTiBA accreditation: No
Delivery method: Campus
Total tuition: $13,604.98 in-state | $26,969.98 out-of-state
2026 Cost per credit: $412.27 in-state | $817.27 out-of-state
Credits: 33
Learn more: Program detailsUniversity of Wisconsin–Madison
Madison, WisconsinProgram: Master of Science in Electrical and Computer Engineering: Machine Learning and Signal Processing
ARTiBA accreditation: No
Delivery method: Campus
Total tuition: $33,000
2026 Cost per credit: $1,100
Credits: 30
Learn more: Program detailsUniversity of Maryland
College Park, MarylandProgram: Master of Professional Studies in Machine Learning
ARTiBA accreditation: Yes
Delivery method: Campus
Total tuition: $43,120
2026 Cost per credit: $1,437.33
Credits: 30
Learn more: Program detailsUniversity of Arizona
Tucson, ArizonaProgram: Master of Science in Information Science (Machine Learning Subplan)
ARTiBA accreditation: No
Delivery method: Online & campus
Total tuition: $34,290 in-state | $60,056.70 out-of-state
2026 Cost per credit: $1,143 in-state | $2,001.89
Credits: 30
Learn more: Program detailsUniversity of Massachusetts Lowell
Lowell, MassachusettsProgram: Master of Science in Engineering - Computer Engineering (Artificial and Machine Intelligence Concentration)
ARTiBA accreditation: No
Delivery method: Campus
Total tuition: $28,393.20 in-state | $48,830.10 out-of-state
2026 Cost per credit: $946.44 in-state | $1,627.67 out-of-state
Credits: 30
Learn more: Program detailsDuke University
Durham, North CarolinaProgram: Master of Science in Electrical and Computer Engineering (Data Analytics & Machine Learning Concentration)
ARTiBA accreditation: Yes
Delivery method: Campus
Total tuition: $73,057
2026 Cost per credit: $2,435
Credits: 30
Learn more: Program detailsNorthwestern University
Evanston, IllinoisProgram: Master of Science in Machine Learning and Data Science
ARTiBA accreditation: Yes
Delivery method: Campus
Total tuition: $245,190
2026 Cost per credit: $8,173
Credits: 30
Learn more: Program detailsStevens Institute of Technology
Hoboken, New JerseyProgram: Master of Science in Machine Learning
ARTiBA accreditation: No
Delivery method: Online & campus
Total tuition: $59,790
2026 Cost per credit: $1,993
Credits: 30
Learn more: Program detailsDrexel University
Philadelphia, PennsylvaniaProgram: Master of Science in Artificial Intelligence and Machine Learning
ARTiBA accreditation: No
Delivery method: Online & campus
Total tuition: $66,645
2026 Cost per credit: $1,481
Credits: 45
Learn more: Program details
These rankings were compiled from data accessed in January 2026 from Integrated Post-Secondary Education Data System (IPEDS) and College Navigator (both services National Center for Education Statistics). Tuition data was pulled from individual university websites and is current as of January 2026. If available, we also use additional criteria such as accreditation or designations by outside organizations or agencies.
2025 Campus Rankings
2024 Campus Rankings
Best Online Master’s in Machine Learning Programs for 2026
Drexel University
Philadelphia, PennsylvaniaProgram: Online Master of Science in Artificial Intelligence and Machine Learning
ARTiBA Accreditation: No
Credits: $45
Total tuition: $66,645
2026 Cost per credit: $1,481
Delivery Method: Campus & online
GRE Required: Recommended for international students and for domestic students with a GPA below 3.0
Learn more: Program detailsMercer University
Macon, GeorgiaProgram: Online Master of Science in Applied Data Intelligence and Machine Learning
ARTiBA Accreditation: No
Credits: $30
Total tuition: $21,930
2026 Cost per credit: $731
Delivery Method: Online
GRE Required: Not required
Learn more: Program detailsStevens Institute of Technology
Hoboken, New JerseyProgram: Online Master of Science in Machine Learning
ARTiBA Accreditation: No
Credits: $30
Total tuition: $59,790
2026 Cost per credit: $1,993
Delivery Method: Campus & online
GRE Required: Required
Learn more: Program detailsUniversity of Arizona
Tucson, ArizonaProgram: Online Master of Science in Information Science (Machine Learning Subplan)
ARTiBA Accreditation: No
Credits: $30
Total tuition: $24,000 in-state | $54,060 out-of-state
2026 Cost per credit: $800 in-state | $1,802 out-of-state
Delivery Method: Campus & online
GRE Required: Not required
Learn more: Program detailsColorado State University Global
Aurora, ColoradoProgram: Online Master of Science in Artificial Intelligence and Machine Learning
ARTiBA Accreditation: No
Credits: $30
Total tuition: $20,250
2026 Cost per credit: $675
Delivery Method: Online
GRE Required: Not required
Learn more: Program detailsColumbia University
New York, New YorkProgram: Online Master of Science in Computer Science - Machine Learning Track
ARTiBA Accreditation: Yes
Credits: $30
Total tuition: $81,000
2026 Cost per credit: $2,700
Delivery Method: Online
GRE Required: Optional, but not required
Learn more: Program details
These rankings were compiled from data accessed in January 2026 from Integrated Post-Secondary Education Data System (IPEDS) and College Navigator (both services National Center for Education Statistics). Tuition data was pulled from individual university websites and is current as of January 2026. If available, we also use additional criteria such as accreditation or designations by outside organizations or agencies.
2025 Online Rankings
Online vs. Campus Master’s in Machine Learning Programs
An online master’s in machine learning can be a strong choice for working professionals, remote students, and students who want to avoid relocation. Campus programs may be better for students who want research labs, in-person faculty mentorship, assistantships, internships, and stronger peer networking.
| Format | Best for | Benefits | Watch-outs |
| Online master’s in machine learning | Working professionals, remote students, students avoiding relocation | Flexible scheduling, part-time options, lower relocation cost | Less in-person research access, fewer lab or peer-networking opportunities |
| Campus ML master’s | Full-time students, research-focused students, international students seeking campus recruiting | Faculty access, research labs, internship pipelines, peer network | Relocation cost, opportunity cost, less schedule flexibility |
| Hybrid ML program | Students near campus who want flexibility | Combines online convenience with in-person support | Travel requirements and scheduling conflicts |
| CS master’s with ML track | Students who want broader technical range | Strong CS foundation plus ML electives | May include fewer ML-specific courses than a dedicated ML degree |
| Professional AI/ML degree | Industry-focused students | Applied projects, capstones, deployment skills | May be less ideal for PhD preparation |
When comparing online programs, verify whether online students take the same courses, learn from the same faculty, earn the same diploma, and receive the same career support as campus students.
Common Master’s in Machine Learning Curriculum
Strong machine learning graduate programs should combine math, statistics, programming, algorithms, systems, model evaluation, deployment, and responsible AI.
ACM/IEEE-CS/AAAI’s CS2023 guidance reflects the growing role of artificial intelligence, data management, security, software development, ethics, and mathematical foundations in computer science education.
Foundational Topics
Machine learning master’s students often need preparation in:
- Linear algebra
- Calculus
- Probability and statistics
- Optimization
- Algorithms and data structures
- Database systems
- Software engineering
- Distributed systems
- Cloud computing
- Experimental design
- Data ethics and privacy
Core Machine Learning Topics
Common graduate ML courses may cover:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep learning
- Natural language processing
- Computer vision
- Recommender systems
- Bayesian machine learning
- Probabilistic graphical models
- Model evaluation and validation
- Causal inference
- Fairness, bias, and interpretability
Modern 2026 ML and AI Skills
For 2026, stronger programs should also address:
- Generative AI and large language models
- Retrieval-augmented generation
- Fine-tuning and evaluation
- Vector databases and embeddings
- MLOps and model deployment
- Model monitoring and drift detection
- Data pipelines and feature stores
- AI-assisted software development
- Secure and privacy-preserving ML
- Responsible AI, governance, and model risk management
Responsible AI deserves explicit coverage. Stanford HAI’s 2026 AI Index reports that responsible AI benchmarking is not keeping pace with AI advances and that documented AI incidents rose to 362 in 2025, up from 233 in 2024. (Stanford HAI)
Admissions Requirements for a Master’s in Machine Learning
Admissions requirements vary by school, but most machine learning master’s programs expect applicants to show readiness in programming, mathematics, statistics, and computer science.
Common Admissions Requirements
Applicants may need:
- Bachelor’s degree from an accredited institution
- Preferred background in computer science, data science, statistics, mathematics, engineering, physics, economics, or another quantitative field
- Programming experience, usually Python plus one or more of Java, C++, R, Julia, or SQL
- Data structures and algorithms
- Linear algebra
- Calculus
- Probability and statistics
- Discrete math
- Computer systems or software engineering background
- Statement of purpose
- Resume or CV
- Letters of recommendation
- GRE, if required, optional, or conditionally waived
- TOEFL/IELTS for many international applicants
- Portfolio, GitHub, research, publications, or professional projects when optional
Some programs are designed for applicants who already have a strong computer science or engineering background. Others offer bridge courses or prerequisite pathways for students from related quantitative fields.
Can You Get a Master’s in Machine Learning Without a CS Degree?
Yes, some students can enter a master’s in machine learning without a computer science degree, but they usually need to prove readiness in programming, math, and computing fundamentals.
Good preparation options include:
- Bridge programs
- Post-baccalaureate CS coursework
- Community college prerequisite courses
- Graduate certificates in CS, data science, AI, or applied statistics
- MOOCs with graded projects
- Python, SQL, data structures, algorithms, and linear algebra preparation
- GitHub projects
- Research assistant experience
- Professional software, analytics, or data engineering experience
Applicants from mathematics, statistics, engineering, physics, economics, data analytics, or information technology may be competitive if they fill gaps in programming, data structures, algorithms, and systems.
Career changers should compare programs carefully. A program that admits non-CS students but does not provide structured foundation courses may be harder than a program with a clear bridge pathway.
How Much Does a Master’s in Machine Learning Cost?
The cost of a master’s in machine learning varies widely by university, format, credit requirements, residency status, and fee structure. Do not rely only on advertised tuition. Some programs charge per credit, some charge per unit, some charge by semester, and some use a flat program tuition model.
Do not publish a fixed cost range unless every program card has been manually verified against official university tuition pages.
What to Include in Total Cost
When comparing programs, include:
- Tuition per credit, unit, semester, or term
- Required credits or units
- Graduate fees
- Technology fees
- Online learning fees
- Books and software
- Laptop or hardware requirements
- Cloud computing credits
- Lab or practicum fees
- Travel or residency costs
- Relocation and housing for campus programs
- Lost income if attending full time
- Loan interest if borrowing
Cost Formulas
Estimated tuition = required credits × tuition per credit
Estimated total cost = tuition + fees + books/software + travel/residency + opportunity cost
Is a Master’s in Machine Learning Worth It?
A master’s in machine learning can be worth it for students pursuing AI/ML engineering, applied research, data science, computer vision, NLP, recommender systems, robotics, or MLOps roles.
However, ROI depends on total tuition, whether the student keeps working while enrolled, employer tuition reimbursement, prior technical experience, internship access, location, and whether the program helps the student build portfolio-ready projects.
Students who already have strong CS and ML experience may get better ROI from a lower-cost online program, a graduate certificate, targeted AI certifications, or a project-based portfolio.
A Machine Learning Master’s May Be Worth It If You Are:
- A software engineer moving into ML engineering or AI engineering
- A data analyst or data scientist moving into more technical ML roles
- A student targeting applied scientist or research roles
- A career changer who has completed prerequisites first
- A student interested in NLP, computer vision, generative AI, or MLOps
- A professional whose employer offers tuition reimbursement
- A student considering a PhD or research-focused role
A Machine Learning Master’s May Not Be Necessary If You Are:
- A developer who can advance through experience and portfolio work
- A student who only needs one narrow ML skill
- An applicant who would take on high debt without a clear career plan
- A learner who may be better served by certificates, bootcamps, or targeted AI/ML courses
- A student who wants business analytics roles rather than technical ML roles
Avoid broad claims that the degree will “break even” quickly or guarantee hiring by top technology companies. Those outcomes depend on program cost, job market timing, student background, portfolio quality, location, and employer demand.
Master’s in Machine Learning Jobs and Salary Outlook
Machine learning is not always a standalone BLS occupation, so salary research should use a mix of BLS-adjacent roles and employer/job-posting analysis. Relevant public BLS roles include data scientist, computer and information research scientist, software developer, information security analyst, operations research analyst, and statistician.
| Career path | Relevant public data source | 2024 median pay / outlook to verify | How an ML master’s can help |
| Machine learning engineer | BLS-adjacent roles plus employer/job-posting analysis | Salary varies because ML engineer is not always a standalone BLS occupation | Model development, deployment, evaluation, MLOps, software engineering |
| Data scientist | BLS Data Scientists | $112,590 median pay; 34% growth from 2024–2034 | Statistical modeling, experimentation, ML, data systems |
| Computer and information research scientist | BLS Computer and Information Research Scientists | $140,910 median pay; 20% growth from 2024–2034 | Research methods, algorithms, AI theory, PhD preparation |
| Software developer / AI software engineer | BLS Software Developers, QA Analysts, and Testers | Software developers: $133,080 median pay; overall group growth of 15% from 2024–2034 | Production software, algorithms, ML integration, backend systems |
| Information security analyst with AI focus | BLS Information Security Analysts | $124,910 median pay; 29% growth from 2024–2034 | ML security, anomaly detection, secure AI systems |
| Operations research analyst | BLS Operations Research Analysts | $91,290 median pay; verify latest outlook at publication | Optimization, predictive modeling, decision systems |
| Statistician / quantitative analyst | BLS Mathematicians and Statisticians | Statisticians: $103,300 median pay; mathematicians: $121,680 median pay | Statistical learning, inference, model validation |
BLS reports strong 2024–2034 growth for several machine learning-related roles, including data scientists, computer and information research scientists, software developers, and information security analysts.
However, salary varies by experience, employer, industry, location, specialization, portfolio strength, internship history, and whether the graduate moves into a higher-level technical role.
A master’s degree alone does not guarantee a machine learning engineering role. Employers commonly look for strong programming skills, a solid foundation in math, experience evaluating models, software engineering skills, and deployed projects.
How to Choose the Right Machine Learning Master’s Program
Use this checklist before applying:
- Does the program match your target role?
- Does it emphasize ML theory, applied ML, or production ML?
- Does it include enough math, statistics, and algorithms?
- Does it offer your preferred specialization, such as NLP, computer vision, generative AI, robotics, recommender systems, or MLOps?
- Is the program thesis-based, capstone-based, practicum-based, or coursework-only?
- Can you enroll part time?
- Is the GRE required, optional, or waived?
- Are prerequisites clearly listed?
- Is tuition transparent?
- Are online students eligible for the same courses and faculty?
- Does the program offer career support?
- Does the school publish outcomes or employer connections?
- Are assistantships, scholarships, or employer reimbursement available?
- Is the institution regionally accredited?
- Does the program help students build portfolio-ready projects?
Portfolio Projects for Machine Learning Master’s Students
Portfolio work is especially important for students without prior ML experience. A strong ML project should include code, documentation, data notes, model limitations, evaluation metrics, reproducibility steps, deployment notes, and ethical considerations.
| Career goal | Portfolio project ideas |
| Machine learning engineer | End-to-end ML pipeline, model deployment API, model monitoring dashboard, feature store project |
| AI engineer | RAG application, LLM evaluation tool, fine-tuning experiment, AI agent with safety guardrails |
| Data scientist | Predictive model, A/B test analysis, causal inference project, forecasting dashboard |
| Computer vision engineer | Object detection app, medical image classifier, video analytics pipeline |
| NLP engineer | Document classifier, semantic search engine, chatbot evaluation framework |
| MLOps engineer | CI/CD pipeline for ML, containerized model serving, drift detection system |
| Research-focused student | Reproducibility study, conference-style paper, thesis project, benchmark comparison |
Students should connect portfolio projects to target job descriptions. For example, ML engineering roles often require deployment, monitoring, testing, and data pipeline experience, not just model training notebooks.
Frequently Asked Questions
A master’s in machine learning can be worth it if it helps you move into a specific technical role, such as machine learning engineer, AI engineer, data scientist, applied scientist, computer vision engineer, NLP engineer, or MLOps engineer. The degree is most valuable when the curriculum includes strong math, programming, model evaluation, deployment, and portfolio projects. It may be less worthwhile if the program is expensive, duplicates skills you already have, or does not connect to your target job. ROI depends on tuition, opportunity cost, employer reimbursement, prior experience, internships, location, and job market conditions.
Graduates may pursue roles in machine learning engineering, AI engineering, data science, applied research, NLP, computer vision, recommender systems, robotics, MLOps, and AI product development. Some graduates work on model training and evaluation, while others focus on deploying models into production systems. Research-focused graduates may continue into PhD programs or applied scientist roles. The best path depends on the program’s curriculum and the student’s project work. Students targeting industry roles should build production-ready projects, not just coursework notebooks.
The cost of a master’s in machine learning varies widely by university, delivery format, residency status, credit requirements, and fees. Some programs charge per credit or unit, while others charge by semester or use a flat program rate. Students should calculate tuition by multiplying required credits by tuition per credit, then add fees, software, books, cloud computing costs, travel, residency requirements, and opportunity cost. Full-time campus students should also consider housing and lost income. Before publication, every TechGuide program card should be verified against official university tuition pages.
Most machine learning master’s programs take one to two years for full-time students and two to four years for part-time students. Students who need prerequisite coursework may need additional time before beginning advanced ML classes. Thesis-based programs can also take longer than coursework-only or capstone-based programs. Working professionals should look for part-time pacing, predictable course rotation, asynchronous options, and clear maximum time-to-completion policies. Students aiming for research or PhD preparation should consider whether the extra time for a thesis or research practicum is worth it.
Yes, some universities offer online or hybrid machine learning, AI, computer science, or data science master’s programs with substantial ML coursework. Online programs can be a strong fit for working professionals and remote students. However, students should verify whether online learners take the same courses, learn from the same faculty, receive the same degree name, and access the same career support as campus students. Online students should also check whether the program includes synchronous class meetings, proctored exams, required residencies, group projects, or cloud computing requirements.
Not always, but most applicants need strong preparation in programming, mathematics, statistics, and computer science fundamentals. Students from math, engineering, statistics, physics, economics, data analytics, or information technology backgrounds may qualify if they can demonstrate readiness. Common prerequisites include Python, data structures, algorithms, linear algebra, calculus, probability, statistics, and sometimes computer systems. Applicants without a CS degree should look for programs with bridge courses, prerequisite pathways, or clear guidance for non-CS students. A portfolio or GitHub project can also help show technical readiness.
Common prerequisites include programming, data structures, algorithms, linear algebra, calculus, probability, statistics, and discrete math. Some programs also expect experience with databases, computer systems, software engineering, or research methods. Python is especially important because it is widely used in ML coursework and projects, but Java, C++, R, Julia, and SQL may also be useful. Applicants should review each program’s prerequisite list carefully. A program that assumes advanced math and coding experience may be difficult for career changers unless they complete foundation coursework first.
Machine learning is usually more focused, while artificial intelligence is broader. A machine learning master’s may be better for students who want depth in statistical learning, deep learning, model evaluation, optimization, and deployment. A master’s in artificial intelligence may be better for students who want a broader AI curriculum that includes ML, NLP, computer vision, robotics, reasoning, planning, and responsible AI. The best choice depends on the program’s courses and your target role. For ML engineering roles, either degree can work if it includes strong programming, math, and deployment projects.
A machine learning master’s is usually better for students who want technical ML, AI engineering, model development, or applied research roles. A data science master’s may be better for students who want roles involving analytics, experimentation, data visualization, business decision-making, and applied modeling. The fields overlap, but emphasis matters. ML programs often go deeper into algorithms, deep learning, optimization, and deployment. Data science programs often include more statistics, data wrangling, communication, and domain analytics. Students should compare course catalogs and target job descriptions before choosing.
Some machine learning master’s programs require the GRE, but many list it as optional, waived, or not required. Policies vary by school, applicant background, GPA, degree format, and application cycle. A strong GRE quantitative score may help some applicants, especially those from nontraditional backgrounds, but it is not always necessary. Programs may place more weight on prior coursework, programming experience, recommendations, statement of purpose, research experience, and technical projects. Always check the official program page before applying because GRE policies can change.
The best specialization depends on your career goal. AI engineering students may focus on deep learning, NLP, computer vision, generative AI, and model deployment. Data science students may focus on statistics, experimentation, causal inference, and predictive modeling. MLOps students should look for cloud computing, CI/CD, model monitoring, data pipelines, and feature stores. Research-focused students may prefer optimization, probabilistic modeling, reinforcement learning, or theory-heavy courses. Students interested in responsible AI should look for fairness, interpretability, safety, privacy, governance, and model risk management.
Build projects that match your target role. Machine learning engineers can build end-to-end ML pipelines, deployment APIs, monitoring dashboards, or feature store projects. AI engineers can build RAG applications, LLM evaluation tools, fine-tuning experiments, or agent prototypes with safety controls. Data scientists can build predictive models, A/B test analyses, causal inference projects, or forecasting dashboards. NLP students can build semantic search tools or document classifiers. Strong projects should include clean code, documentation, metrics, reproducibility steps, model limitations, deployment details, and ethical considerations.
Machine Learning Master’s School Listings
Campus Programs
Carnegie Mellon University
Pittsburgh, PennsylvaniaProgram: Master of Science in Machine Learning
ARTiBA accreditation: Yes
Delivery method: Campus
Total tuition: $31,500
2026 Cost per credit: $1,050
Credits: 30
GRE requirement: Required
Learn more: Program detailsClaremont Graduate University
Claremont, CaliforniaProgram: Master of Science in Statistics & Machine Learning
ARTiBA accreditation: No
Delivery method: Campus
Total tuition: $64,640
2026 Cost per credit: $2,070
Credits: 32
GRE requirement: Optional
Learn more: Program detailsDrexel University
Philadelphia, PennsylvaniaProgram: Master of Science in Artificial Intelligence and Machine Learning
ARTiBA accreditation: No
Delivery method: Online & campus
Total tuition: $66,645
2026 Cost per credit: $1,481
Credits: 45
GRE requirement: Recommended for international students and for domestic students with a GPA below 3.0
Learn more: Program detailsDuke University
Durham, North CarolinaProgram: Master of Science in Electrical and Computer Engineering (Data Analytics & Machine Learning Concentration)
ARTiBA accreditation: Yes
Delivery method: Campus
Total tuition: $73,057
2026 Cost per credit: $2,435
Credits: 30
GRE requirement: Not required
Learn more: Program detailsNorthwestern University
Evanston, IllinoisProgram: Master of Science in Machine Learning and Data Science
ARTiBA accreditation: Yes
Delivery method: Campus
Total tuition: $245,190
2026 Cost per credit: $8,173
Credits: 30
GRE requirement: Optional
Learn more: Program detailsStevens Institute of Technology
Hoboken, New JerseyProgram: Master of Science in Machine Learning
ARTiBA accreditation: No
Delivery method: Online & campus
Total tuition: $59,790
2026 Cost per credit: $1,993
Credits: 30
GRE requirement: Required
Learn more: Program detailsUniversity of Arizona
Tucson, ArizonaProgram: Master of Science in Information Science (Machine Learning Subplan)
ARTiBA accreditation: No
Delivery method: Online & campus
Total tuition: $34,290 in-state | $60,056.70 out-of-state
2026 Cost per credit: $1,143 in-state | $2,001.89
Credits: 30
GRE requirement: Not required
Learn more: Program detailsUniversity of Maryland
College Park, MarylandProgram: Master of Professional Studies in Machine Learning
ARTiBA accreditation: Yes
Delivery method: Campus
Total tuition: $43,120
2026 Cost per credit: $1,437.33
Credits: 30
GRE requirement: Optional
Learn more: Program detailsUniversity of Massachusetts Lowell
Lowell, MassachusettsProgram: Master of Science in Engineering - Computer Engineering (Artificial and Machine Intelligence Concentration)
ARTiBA accreditation: No
Delivery method: Campus
Total tuition: $28,393.20 in-state | $48,830.10 out-of-state
2026 Cost per credit: $946.44 in-state | $1,627.67 out-of-state
Credits: 30
GRE requirement: Required
Learn more: Program detailsUniversity of North Texas
Denton, TexasProgram: Master of Science in Artificial Intelligence (Machine Learning Concentration)
ARTiBA accreditation: No
Delivery method: Campus
Total tuition: $13,604.98 in-state | $26,969.98 out-of-state
2026 Cost per credit: $412.27 in-state | $817.27 out-of-state
Credits: 33
GRE requirement: Not required
Learn more: Program detailsUniversity of Wisconsin–Madison
Madison, WisconsinProgram: Master of Science in Electrical and Computer Engineering: Machine Learning and Signal Processing
ARTiBA accreditation: No
Delivery method: Campus
Total tuition: $33,000
2026 Cost per credit: $1,100
Credits: 30
GRE requirement: Not required
Learn more: Program details
Online Programs
Colorado State University Global
Aurora, ColoradoProgram: https://csuglobal.edu/academic-programs/graduate-degrees/masters-science-degree-artificial-intelligence-machine-learning
ARTiBA Accreditation: No
Credits: $30
Total tuition: $24,300
2025/2026 Cost per credit: $675
Delivery Method: Online
GRE Required: Not requiredColumbia University
New York, New YorkProgram: https://www.cvn.columbia.edu/program/columbia-university-computer-science-masters-degree-machine-learning-masters-science
ARTiBA Accreditation: Yes
Credits: $30
Total tuition: $81,000
2025/2026 Cost per credit: $2,700
Delivery Method: Online
GRE Required: Optional, but not requiredDrexel University
Philadelphia, PennsylvaniaProgram: https://drexel.edu/cci/academics/masters-programs/ms-in-artificial-intelligence-machine-learning/
ARTiBA Accreditation: No
Credits: $45
Total tuition: $66,645
2025/2026 Cost per credit: $1,481
Delivery Method: Campus & online
GRE Required: Recommended for international students and for domestic students with a GPA below 3.0Stevens Institute of Technology
Hoboken, New JerseyProgram: https://www.stevens.edu/program/machine-learning-masters
ARTiBA Accreditation: No
Credits: $30
Total tuition: $59,970
2025/2026 Cost per credit: $1,993
Delivery Method: Campus & online
GRE Required: RequiredUniversity of Arizona
Tucson, ArizonaProgram: https://gl.ischool.arizona.edu/ms-information-science-in-machine-learning-university-arizona
ARTiBA Accreditation: No
Credits: $30
Total tuition: $24,003 in-state | $54,051 out-of-state
2025/2026 Cost per credit: $800 in-state | $1,802 out-of-state
Delivery Method: Campus & online
GRE Required: Not required
Expert Advice
Find the latest interviews with subject matter experts and people working at the forefront of their field and get advice on Master’s of Machine Learning directly from some of the world’s leading authorities. Learn more about all the different pathways and opportunities available in tech today.
- How did you first get into machine learning (what kind of degree or work experience led you to the field)?
- What do you think are the most important qualities or qualifications needed to be successful as a machine learning expert?
- What are some of the reasons people become a machine learning expert?
- What should students expect when choosing a machine learning internship?
- What are employers generally looking for when hiring entry-level machine learning expert?
- Do you find that people that train as a machine learning expert stay in the field, or are they finding other, relevant work opportunities?