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The master’s degree is where machine learning exists as a degree title in its own right. An online MS in Machine Learning is designed for students who already hold a bachelor’s degree – commonly in computer science, statistics, mathematics, engineering, or physics – and want concentrated graduate training in the statistical and algorithmic core of the field. Some schools deliver the same content as a machine learning track inside an MS in Computer Science, Data Science, or Artificial Intelligence, which is why the course list matters more than the diploma text.
This page explains how these programs are structured, what prerequisites they assume, how thesis and applied tracks differ, and what to compare across schools.
It is a graduate program that builds advanced training in statistical learning methods, deep learning, and model development and deployment through online coursework. It is the degree level at which “machine learning” is commonly offered as a standalone title.
Master of Science in Machine Learning is the most direct title. Equivalent training is also offered as an MS in Computer Science, MS in Data Science, or MS in Artificial Intelligence with a machine learning track or concentration. Compare required course lists rather than titles.
Typically 30 to 36 semester hours, commonly one to two years full-time and longer part-time. Programs designed for working professionals often stretch across three years at a reduced course load.
Most assume programming fluency plus undergraduate linear algebra, multivariable calculus, and probability and statistics. Applicants without that background are sometimes admitted conditionally or asked to complete bridge coursework first. Confirm the specific expectations before applying.
A thesis track culminates in original research supervised by a faculty member and is the better preparation for doctoral study or research roles. An applied or capstone track culminates in a substantial project and is generally the better fit for industry practice. Many programs offer both.
A completed bachelor’s degree, transcripts, a resume, and a statement of purpose are standard. Some programs request letters of recommendation, evidence of programming and math coursework, or GRE scores, though many online programs are test-optional.
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 |
|---|---|
| Statistical Learning Theory | Why learning algorithms work, generalization bounds, bias and variance |
| Advanced Supervised Learning | Regularized regression, tree ensembles, kernel methods, and model selection |
| Unsupervised & Representation Learning | Clustering, dimensionality reduction, and learned representations |
| Deep Learning | Network architectures, optimization, and applications to vision, sequence, and language tasks |
| Probabilistic Modeling & Inference | Bayesian methods, graphical models, and reasoning under uncertainty |
| Machine Learning Systems and Deployment | Pipelines, serving infrastructure, experiment tracking, and monitoring for drift |
| Fairness, Interpretability & Ethics | Auditing models for bias, explaining predictions, and evaluating deployment risk |
| Thesis or Applied Capstone | Original research, or an end-to-end project taken from problem framing to a validated model |
Outcomes vary by program, but you can compare:
For pacing and delivery comparisons, see: How Online Machine Learning 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. Many programs also want evidence that you can handle the mathematics – typically prior coursework in linear algebra, calculus, and probability and statistics, or demonstrated equivalent experience.
Applicants coming from outside computing and mathematics are not automatically excluded. Some programs admit them with conditional status, a required bridge sequence, or a recommendation to complete prerequisite coursework first. Ask admissions directly what they expect rather than inferring it from the catalog.
A bachelor’s pathway – typically a computer science, data science, or artificial intelligence major with machine learning coursework – is a workable entry point into engineering-side roles that build and serve models. A master’s is more commonly pursued to enter applied modeling and research roles, to pivot into machine learning from another quantitative field, or to reach the theory depth that harder modeling problems require.
Compare degree options:
For a value and fit discussion, see: Is an Online Machine Learning Degree Worth It. For the broader field, see the Artificial Intelligence Program Guide.
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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