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An online master’s in artificial intelligence is designed for students who already hold a bachelor’s degree and want advanced training in machine learning, deep learning, and the applied branches of AI. Programs vary in whether they emphasize research preparation, applied engineering, or a specific domain such as language, vision, or robotics.
The master’s level is where AI-titled degrees are most established. Where a BS in Artificial Intelligence is still a newer offering, graduate programs in AI, machine learning, and computer science with an AI focus have been widely available longer, and online delivery at this level is common.
It is a graduate program that builds advanced machine learning, deep learning, and applied AI knowledge through online coursework, usually awarded as an MS in Artificial Intelligence.
Common options include an MS in Artificial Intelligence, an MS in Machine Learning, or an MS in Computer Science with an AI specialization. Compare the required course lists, since the three often overlap heavily.
An AI master’s is usually broader, covering machine learning alongside reasoning and search, natural language processing, vision, and ethics. A machine learning master’s concentrates more narrowly on statistical learning and modeling methods.
Most programs include a core in machine learning, deep learning, and probabilistic methods, plus electives or a specialization and either a capstone project or a thesis.
Most programs require a completed bachelor’s degree, transcripts, a resume, and a statement of purpose. Many also expect prior coursework in programming, linear algebra, and probability, or offer bridge courses to cover it.
Not always. Many programs admit applicants from mathematics, statistics, engineering, physics, and economics, and some offer bridge or foundation coursework for applicants without a computing degree. What they generally do expect is demonstrated programming and quantitative background.
Every school list on this site is ordered by the BOC Score, computed from the most recent school-level data published by the U.S. Department of Education (College Scorecard and IPEDS). To qualify, a school must be currently operating and accredited by an agency recognized by the U.S. Department of Education. Each eligible school is then scored on five measures, percentile-ranked against schools at the same credential level:
Schools without enough outcome data appear after ranked schools, without a score. Advertising never affects these rankings. Read the full methodology.
Source:IPEDSCollege Scorecard
Source:Accreditor: Western Association of Schools and Colleges Senior Colleges and University CommissionIPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
Source:IPEDSCollege Scorecard
| Course Topic | What You Learn |
|---|---|
| Advanced Machine Learning | Statistical learning theory, model selection, and regularization |
| Deep Learning | Network architectures, training dynamics, and transfer learning |
| Probabilistic Graphical Models | Bayesian methods, inference, and reasoning under uncertainty |
| Natural Language Processing | Language modeling, representation, and sequence tasks |
| Computer Vision | Image representation, detection, and recognition methods |
| Reinforcement Learning | Sequential decision making, policies, and reward design |
| AI Ethics and Governance | Fairness, accountability, explainability, and regulatory frameworks |
| Capstone or Thesis | Applied project or original research with a faculty advisor |
This is the practical hurdle at the graduate level. AI master’s coursework assumes fluency in programming and comfort with linear algebra and probability, and programs handle applicants who lack that background in different ways. Some require prerequisite courses before matriculation. Some admit conditionally and require bridge courses in the first term, which extend the program. Some assume you will fill the gaps on your own and let you struggle if you do not.
Before applying, ask each school which specific prerequisites apply to you given your undergraduate transcript, whether bridge courses count toward the degree credits or sit on top of them, and how long the bridge sequence takes.
Programs generally end in one of three ways, and the choice matters for what comes next:
If research-track roles interest you, confirm the program offers a thesis option and that online students have genuine access to faculty advising, not only on-campus students.
Outcomes vary by program, but you can compare:
For pacing and delivery comparisons, see: How Online AI Degrees Work
Requirements vary by school, but most programs require a completed bachelor’s degree. Common elements include transcripts, a resume, and a statement of purpose. Some programs request GRE scores or letters of recommendation, though many online programs are test-optional. Programs commonly expect prior coursework or demonstrated competence in programming, linear algebra, and probability.
A bachelor’s in AI is generally the entry credential for engineering-track roles that build and deploy models. A master’s is typically pursued to reach research-oriented positions, to pivot into AI from another quantitative field, or to qualify for data science roles that commonly expect graduate coursework. Research titles in this field, particularly computer and information research scientist positions, often expect a doctorate rather than a master’s.
Compare degree options and routes:
For a value and fit discussion, see: Is an Online AI Degree Worth It
Data verified: August 10, 2026. Salary, employment, and tuition figures on this page are sourced from the U.S. Bureau of Labor Statistics (OEWS May 2025; Employment Projections 2024–2034) and the U.S. Department of Education College Scorecard (2023 cohort). The source agency and data year are cited inline with every statistic.
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