5 Best AI Masters Degrees in Maryland

You need to evaluate nationally recognized institutions with strong AI credentials when selecting AI Masters programs in Maryland. To name just one example, the University of Maryland ranks No. 3 among all U.S. universities in artificial intelligence based on recent publications and No. 8 among public institutions for the AI specialty. Maryland offers options from research-focused programs to flexible formats designed for working professionals. This guide explores the best AI Masters programs at five Maryland universities and includes details on curriculum, admission requirements, tuition and career outcomes that help you decide.

1. University of Maryland – M.S. in Artificial Intelligence

Program Overview and Key Features

The M.S. in Artificial Intelligence at University of Maryland is one of the best AI master’s programs available. The Science Academy administers it with the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) within the College of Computer, Mathematical, and Natural Sciences. This 30-credit, non-thesis program stands out by combining technical courses in AI fundamentals with coursework that addresses the interaction between AI, humans, and society.

You’ll build solid foundations in mathematics, statistics, and computing while getting a wider view of human-centered AI and what it means for society. The curriculum focuses on machine learning, deep learning, and AI-driven decision-making among areas such as AI ethics, human-computer interaction, explainable AI, and policy. The program prepares you to develop AI solutions that boost human well-being and promote fairness while merging naturally into social and professional contexts.

The program focuses on practical knowledge and applied learning, not research opportunities. Classes occur through face-to-face instruction at the UMD College Park campus, mostly in the evenings to accommodate working professionals. The first cohort in fall 2025 started with about 20 students, though future cohorts are expected to reach 50-100 students. You can complete the program in less than two years and position yourself as a collaborative, adaptable problem solver in this faster evolving field.

Admission Requirements

Your application to this program must meet both general Graduate School requirements and program-specific criteria. You need a four-year baccalaureate degree from a regionally accredited U.S. institution or equivalent, with a minimum 3.0 GPA on a 4.0 scale in all prior undergraduate and graduate coursework.

General requirements include a statement of purpose, official transcripts, and TOEFL/IELTS/PTE scores for international graduate students. Program-specific requirements add depth to your application. You’ll need a CV or resume and a description of your research or work experience. One optional letter of recommendation can strengthen your application.

Prior coursework that establishes quantitative ability proves necessary. This includes calculus, linear algebra, and basic statistics. Just as important, you must demonstrate proficiency in programming languages through either prior programming coursework or substantial software development experience. The GRE remains optional.

Application deadlines differ based on your residency status:

Applications receive rolling review and take only a few weeks for decisions. Submit early rather than wait until the deadline. This allows better preparation before the semester begins.

Curriculum and Core Courses

The 30-credit, 10-course structure focuses on acquiring skills and knowledge needed for careers in information-based industries. You can follow either a full-time track taking three 3-credit courses per semester or a part-time track with two 3-credit courses per semester.

The curriculum builds technical competencies while exploring human-centered dimensions of AI. You’ll gain expertise in foundational areas through courses covering machine learning algorithms, deep learning architectures, and AI-driven decision systems. The program integrates courses on AI ethics, explainable AI, and policy frameworks with technical training.

Elective options provide specialization opportunities across a variety of AI applications:

• MSAI632 Generative AI
• MSAI633 Agentic AI
• MSAI634 AI Policy
• MSAI635 Reinforcement Learning
• MSAI604 Introduction to Optimization for AI
• MSAI612 Deep Learning for AI
• MSAI641 Natural Language Processing for AI
• MSAI642 Robotics for AI
• MSAI650 Cloud Computing for AI
• MSAI651 Big Data Analytics for AI

The program follows a course-based structure without an overarching capstone project. You’ll complete projects within individual courses related to ground use cases of what you’re learning instead. Research opportunities aren’t built into the program, though the department allows participation in research opportunities found elsewhere on campus.

Tuition, Duration, and Program Format

Tuition for the program uses a flat rate that doesn’t distinguish between Maryland residents and non-residents. This rate applies to course sections beginning with PCS (in-person) or PWS (online) designations. You can find current tuition and fee information through the official program website.

The program timeline offers flexibility based on your schedule. Full-time students taking three courses per semester can complete the degree in less than two years. Part-time students taking two courses per semester extend the timeline across five semesters but gain the ability to balance professional responsibilities with graduate studies.

UMD College Park campus hosts face-to-face instruction. Evening class schedules accommodate working professionals who maintain full-time employment while pursuing the degree. UMD faculty members and field experts provide instruction and bring both academic rigor and industry view to coursework.

The program attracts a diverse student body. Recent graduates come straight from undergraduate programs. Working professionals seek formal training. Career-changers pivot into AI fields. This mix creates rich classroom dynamics where you’ll interact with peers bringing varied perspectives and experiences.

Career Outcomes and Job Prospects

The University of Maryland’s Department of Computer Science reported a 93% job placement rate for its class of 2024. The median starting salary reached $105,000 among graduates entering the workforce, with most reporting salaries between $88,000 and $127,000. About 175 students received bonuses with a median amount of $10,257.90.

Internship experience plays a substantial role in employment outcomes. Among respondents, 84.4% completed at least one internship while at UMD, with 67.2% completing two or more internships. 62% of job seekers said their internships led directly to job offers.

UMD graduates accept positions across major employers including Amazon, Google, Microsoft, Capital One, Lockheed Martin, and the National Security Agency. Common job titles include software engineer, data analyst, product manager, and systems developer. Geographic placement favors the DMV region. 28% work in Maryland, 25% in Virginia, and 5% in Washington, D.C.. Combined, 62% of graduates work in the greater DMV area.

The MS in AI program prepares you for careers in different fields through applied learning focused on industry needs rather than academic research. You’ll develop skills valued by employers seeking professionals who understand both technical AI capabilities and their ethical, social, and policy implications.

2. Johns Hopkins University – M.S. in Artificial Intelligence

Program Overview and Key Features

Johns Hopkins University developed its M.S. in Artificial Intelligence through collaboration with the Johns Hopkins Applied Physics Laboratory. This created one of the nation’s first online AI master’s programs. The Whiting School of Engineering administers this program through Engineering for Professionals. The program targets engineers who want to advance their expertise in this field.

The curriculum covers AI areas in depth. Computer robotics, natural language processing and image processing form the core. The instruction team sets this program apart: top-level researchers, scientists and engineers from APL and other industry settings bring current practices directly into coursework. John Piorkowski chairs the program. He’s a chief AI architect at APL and makes sure the content reflects ground applications and emerging technologies.

The program balances theoretical concepts with practical knowledge you can apply to actual systems and processes. You’ll describe requirements, drivers, functions, components, interdependencies, risks and quality factors for various AI implementations. The curriculum gives you the advanced skills you need to develop new AI features. Course selection lets you tailor your degree toward specific career goals.

Johns Hopkins now offers a full-time, in-person option at the Hopkins Bloomberg Center in Washington, D.C., at 555 Pennsylvania Avenue. This goes beyond the traditional online format. This represents the Engineering for Professionals program’s first full-time, in-person offering and provides access to an emerging center for graduate AI education with the nation’s capital as your backdrop.

Admission Requirements

Your application must meet general admission requirements that apply to all master’s degree candidates. Your academic background should have an undergraduate or graduate degree in engineering or computer science. The program welcomes applicants from various technical fields though.

Specific prerequisite coursework proves essential:

Provisional admission remains available if your prior education lacks these courses. You complete missing prerequisites while enrolled, either through Johns Hopkins Engineering courses or at another regionally accredited institution. You then transition to full admission status.

Admitted students earned at least a 3.0 GPA on a 4.0 scale in their latter undergraduate studies. GRE scores aren’t required. Official transcripts from all college studies must accompany your application. Additional requirements may apply for international applicants.

A proficiency exam option exists if you possess extensive experience in prerequisite areas without formal college-level coursework. You can opt out of certain prerequisites with successful completion.

Applications are accepted year-round. Terms begin in spring, summer and fall. 78% of enrolled students receive tuition coverage through employer contribution programs. This makes the program financially available for working professionals.

Curriculum and Core Courses

The degree requires ten graduate-level courses that total 30 credits. You must complete them within five years. The structure has four core courses worth 12 credits and six electives worth 18 credits.

Core courses establish your foundation in AI:

• EN.705.623 AI Algorithm Design and Analysis
• EN.705.603 Creating AI-Enabled Systems
• EN.705.601 Applied Machine Learning
• EN.705.605 Introduction to Generative AI or EN.705.608 Applied Generative AI

You must select at least four courses from the AI elective list for electives. Three of those must be at the 700-level. The remaining two electives may come from any Engineering for Professionals program, subject to advisor approval.

The elective catalog spans diverse AI applications. You’ll find courses in Production AI, Product Management for AI, Values and Ethics in Artificial Intelligence and Responsible AI. Advanced options cover Reinforcement Learning, ChatGPT from Scratch: Building and Training Large Language Models, Deep Learning Using Transformers and Introduction to Mechanistic Interpretability. Specialized tracks address Natural Language Processing, robotics, GPU programming, neural networks, computer vision and assured AI and autonomy.

Focus areas guide your course selection based on interests. You’re not required to follow a specific track though. These areas represent related course groupings relevant to particular career directions. They don’t appear as official designations on your transcript or diploma.

Academic policies maintain program rigor. Only one C-range grade (C+, C, or C-) counts toward your degree. At least three courses worth 9 credits must be 700-level. All course selections outside standard AI requirements need advisor approval.

Tuition, Duration, and Program Format

Each course costs around $4,595. The complete master’s degree runs about $46,000, though some sources estimate the total program price at $52,700. These figures represent the full cost for all ten required courses.

You have up to five years to complete the program. This provides flexibility to balance graduate studies with professional and personal commitments. The online format delivers lectures through short video segments. Weekly Zoom meetings connect you with instructors. Courses offer both synchronous and asynchronous options based on the specific class.

The full-time program at the Hopkins Bloomberg Center in Washington, D.C., offers an intensive educational experience for those who prefer in-person instruction. This location provides access to AI courses that span applied machine learning to generative AI to neuromarketing AI. You can design your degree around ultimate career goals.

The program structure accommodates working professionals. Courses feature subject-matter experts who execute the technologies and techniques they teach. This practitioner-led approach keeps instruction current with industry demands and technological advances.

Career Outcomes and Job Prospects

The program prepares you for roles where AI development and architecture skills prove essential. Machine learning engineer positions represent a primary career path. These roles handle development and architecture that creates AI systems. Demand for these positions increases as AI expands across industries.

The curriculum positions you to become a leader in this emerging field. Your background in engineering, computer science, data science, physics or applied mathematics gains better marketability through this specialized training. The program encourages professionals from various technical disciplines to build AI expertise they can apply across sectors.

Instruction from APL researchers and industry practitioners provides direct exposure to trailblazing applications. You’ll learn from those who implement AI solutions in real-world settings. This gives you insights beyond academic points of view. This practitioner focus lines up your education with actual industry needs and emerging opportunities.

The program responds to evolving technology landscapes and broader community needs. Machine learning and AI continue to transform industries. Graduates enter the workforce equipped with both foundational knowledge and practical skills that employers seeking AI capabilities value.

3. University of Maryland Baltimore County (UMBC) – M.S. in Data Science with AI Focus

Program Overview and Key Features

UMBC offers a Master of Professional Studies in Data Science that emphasizes practical applications of machine learning and AI technologies in a variety of industries. Faculty from multiple disciplines bring deep knowledge of ground data analytics applications to the program. This interdisciplinary approach distinguishes the degree from purely theoretical computer science programs and focuses instead on skills you can apply immediately in data-driven roles.

The curriculum balances foundational data science principles with domain-specific applications. Core courses give you understanding through classes that highlight machine learning, data analysis, and data management. The program also introduces ethical and legal implications surrounding data science and prepares you to guide complex professional scenarios where AI systems intersect with privacy, fairness, and regulatory concerns.

You select courses tailored to specific career goals beyond core requirements. The university developed these through shared work with academic departments. This structure allows you to apply skills and techniques from core courses to your own field or area of expertise. Available pathways include Spatial Analytics, Bioinformatics, Healthcare Analytics, Advanced Computer and Analytics, Economics/Econometrics, Management Science, Policy Analysis, and Cybersecurity.

The program serves as an industry-recognized credential that prepares you with technical and management skills you just need for workplace success. Faculty maintain partnerships with institutions throughout the Maryland/DC tech corridor and provide access to internships and hiring pipelines in government, healthcare, finance, and startup ecosystems.

Admission Requirements

Your application requires a bachelor’s degree in any subject with a 3.0 GPA or above on a 4.0 scale. This flexibility welcomes candidates from various academic backgrounds, though prerequisite coursework in quantitative areas remains mandatory.

Candidates must demonstrate previous post-secondary education in calculus, statistics, and simple programming. You need one semester of statistics plus Calculus I or II depending upon your chosen track. Academic experience equivalent to simple programming courses proves essential. Platforms such as Coursera, MOOCs, or edX offer online courses that do not fulfill prerequisite requirements.

International applicants just need English proficiency scores. The program accepts IELTS (minimum 6.5 with no section below 6.5 and sum of sections at least 27.5), TOEFL (minimum 22 with no section below 22), Duolingo (minimum 120), and PTE (minimum 61).

Application deadlines vary by term and residency status. Domestic applicants face an August 1 deadline for fall admission and December 1 for spring. International applicants must apply by May 19 for fall and October 15 for spring. Decisions take 6-8 weeks after the program deadline.

Curriculum and Core Courses

The MPS curriculum emphasizes applied, professional focus over theoretical research. This makes it especially suited when you have roles requiring immediate on-the-job effect such as data engineer, applied data scientist, ML engineer, or analytics lead. Core courses cover statistics, machine learning, data engineering, databases, visualization, cloud computing, and MLOps.

You develop an understanding of simple computing principles behind data science. These include data ingestion, curation, cleaning, and the four Vs of data science: Volume, Variety, Velocity, and Veracity, plus the implicit fifth V of Value. You gain practical, ground industry-relevant experience through applying principles of data science to problems within specific domains expressed through program pathways.

The curriculum’s breadth and tooling map to employer needs. Course content prepares you in Python, R, SQL, cloud services, and big-data frameworks sufficient for industry entry roles. Hands-on projects simulate ground pipelines and build portfolios that demonstrate technical capabilities to prospective employers.

Capstone requirements integrate learning throughout the program. You complete projects with production-oriented work, deployed models or APIs, data pipelines, and clear business effect metrics. This applied focus separates the professional studies degree from research-oriented master’s programs.

Tuition, Duration, and Program Format

First-year tuition totals $24,650. You should budget for expenses including insurance ($2,056), transportation ($2,569), books ($674), and other costs ($1,748) beyond tuition. Total approximate first-year fees reach $31,697 when accounting for all expenses.

The program spans 18 to 24 months and provides flexibility for full-time and part-time enrollment. This shorter timeline compared to traditional research degrees offers a cost-efficient pathway while still teaching practical, employable skills.

UMBC participates in all federal and state student financial aid programs. The Graduate School, Graduate Departments, and the Office of Financial Aid and Scholarships offer scholarships and assistantships. A monthly payment plan enables you to pay allowable semester charges on an installment basis with no interest charges. Allowable charges divide into a maximum of four installments for fall and spring, and three for summer depending on enrollment date.

Career Outcomes and Job Prospects

UMBC achieved a 90.8% career outcome rate for master’s degree recipients and surpassed the national average of 89.4%. This metric defines graduates who secure employment or pursue further education within six months of completion.

Entry-level applied data scientists and data engineers earn between $85,000 and $120,000 depending on location and company. Mid-level professionals with 2-5 years of experience see salaries ranging from $120,000 to $160,000. These figures vary based on location, company size, and specific skills in areas like cloud computing or MLOps.

UMBC’s proximity to Baltimore and the DC corridor offers steady demand for data scientists, analysts, and engineers. Regional market advantages include access to government agencies, defense contractors, healthcare systems, and fintech companies. Graduates secure roles such as data scientist, machine learning engineer, data analyst, business intelligence engineer, data engineer, analytics consultant, and applied research associate.

Time-to-hire varies with prior experience and networking efforts. Students with relevant background including internships or co-op experience plus strong portfolios receive interviews and offers within 3-6 months post-graduation. Career switchers transitioning from non-tech fields may require 6-12+ months depending on prior skill gaps and networking success.

4. Capitol Technology University – M.S. in Artificial Intelligence

Program Overview and Key Features

Capitol Technology University structures its AI education through a Master of Science in Computer Science program featuring three specialized tracks, with Artificial Intelligence and Data Science as one primary concentration. The university sits in the Washington DC metropolitan area within miles of the NSA and NASA Goddard, among major employers that include Amazon and Capital One.

You can complete the 30-credit online program in as little as 1 year, ranking #10 Most Affordable Master’s in Computer Science in the US by Master’s Program Guide. Capitol Tech offers research-intensive programs beyond the MS in Computer Science. The Master of Research (MRes) in Artificial Intelligence costs $20,850 and targets working professionals with AI-related backgrounds who seek specialized expertise in robotics, autonomous systems, or AI ethics.

The curriculum centers on practical skills needed for careers in high-demand fields. Core courses like Predictive Analytics give you data mining and data science methodologies, while Design of Cloud Networks and Services addresses network architecture for business-grade cloud delivery. Database Systems Implementation introduces architecture and implementation issues in indexing and warehousing. Computer Vision and Deep Learning covers modern developments in computer image processing for the AI track.

Students access facilities that support 3D printing, shared platforms that include GitHub and Wevolver, and microcomputers such as Raspberry Pi and Arduino. This hands-on approach helps Capitol Tech’s students secure positions at employers like Honeywell, Lockheed Martin, and DXC Technology.

Admission Requirements

You can apply and be accepted at any time with no GRE or GMAT required. Courses run eight weeks long across six terms throughout the year and allow you to begin taking courses in the next term after filing an application.

You must have an undergraduate degree from an accredited institution with a cumulative GPA of at least 3.0 on a 4.0 scale to be considered. Students not meeting the 3.0 undergraduate cumulative GPA requirements or program prerequisites receive provisional admission, limited to three courses of enrollment. Provisional students must maintain a 3.0 cumulative GPA in their first three graduate courses to achieve full acceptance status.

Curriculum and Core Courses

The MS in Computer Science requires completion of 10 courses for 30 credit hours. Core courses include Database Systems Implementation, Principles of Cybersecurity, Design of Cloud Networks and Services, Predictive Analytics, and Artificial Intelligence.

You complete electives for the Artificial Intelligence and Data Science track that include Statistical Methods in Data Science, Machine Learning and Neural Networks, Big Data, Computer Vision and Deep Learning, and Advanced Artificial Intelligence. Students choosing this track should take CS 701 as a core course.

Tuition, Duration, and Program Format

The per-credit charge for master courses is $650.00 per credit hour for the 2025-2026 academic year, with an additional technology fee of $45.00 per credit hour. Active duty military receive a reduced rate of $350.00 per credit, while retired military pay $550.00 per credit.

The program offers a Hometown Heroes Tuition Discount that provides community college faculty, high school teachers, emergency personnel, and Department of Defense employees a 20% tuition discount for graduate programs. Capitol Tech designed the Blue Ribbon program to provide working professionals not eligible for the Yellow Ribbon Program a funding resource for their master’s program.

Career Outcomes and Job Prospects

Capitol Tech’s graduates earn higher salaries than students from all other Maryland colleges and universities at every academic level according to a 10-year study conducted by the MD Longitudinal Data System Center. Computer science graduates rank at the top of projected highest-paid master’s degree graduates, with an average salary of $81,039.

The degree provides pathways toward careers as machine learning engineer with average base pay of $147,300, data scientist at $131,000 median base pay, and computer vision engineer averaging $158,300. The U.S. Bureau of Labor Statistics anticipates 15% growth in computer and information technology occupations from 2020 to 2030.

5. Towson University – M.S. in Computer Science with AI Concentration

Program Overview and Key Features

Three specialized tracks distinguish Towson University’s Master of Science in Computer Science, with the Data Science track recommended to students interested in data mining and big data analytics. The program provides a solid scientific and technical foundation to pursue doctoral work or advanced positions in business and industry. You can complete the degree in 18 to 24 months, with evening and summer classes accommodating working professionals. Faculty research specialties span artificial intelligence and data mining. An accelerated combined B.S./M.S. program allows you to complete both degrees in a shorter timeframe.

Admission Requirements

Admission proves competitive, with prerequisite courses required to students lacking computer science backgrounds. Both COSC 501 and COSC 502 serve as prerequisites, along with MATH 263 for those without engineering, mathematics, or computer science degrees. Application deadlines fall on August 15 for fall admission and January 7 for spring.

Curriculum and Core Courses

The 30-credit program has core courses: COSC 519 Operating Systems Principles, COSC 578 Database Management Systems I, COSC 600 Advanced Data Structures and Algorithm Analysis, COSC 612 Software Engineering I, and COSC 650 Computer Networks. You complete either a 3-credit project or 6-credit thesis.

Tuition, Duration, and Program Format

First-year tuition totals $23,948. Graduate assistantships within the department provide funding opportunities. Courses meet during late afternoons, evenings, and summers in state-of-the-art facilities.

Career Outcomes and Job Prospects

Graduates secure positions as software developers, web developers, and computer security analysts. Starting salaries average $70,000, with strong placement in the DMV region at employers that include Aberdeen Proving Ground and Annapolis Junction. Department conferences and seminars connect students with local industry leaders.

Next Steps

Maryland offers diverse pathways into AI education. Each serves different needs and career goals. You might choose UMD’s human-centered approach and top-tier research credentials. Or you could select JHU’s practitioner-led online format to get maximum flexibility. UMBC provides strong data science foundations with pathway customization, while Capitol Tech delivers affordability and rapid completion timelines. Towson rounds out traditional computer science training with AI specialization.

Most programs report 90%+ placement rates and six-figure starting salaries. Your choice should match your learning style and schedule constraints rather than rankings alone. The DMV region’s tech corridor ensures strong employment prospects whatever program you select. Review each program’s curriculum details and reach out to admissions offices. Connect with current students before you make your final decision.