Best Machine Learning Programs in New York 2025

New York offers 44 bachelor's-level machine learning programs. The top schools for undergraduate study are Cornell University, Columbia University in the City of New York, Stony Brook University. These programs prepare students for entry-level positions paying approximately $91,497 in the New York market.

A bachelor's in machine learning typically requires 120-128 credit hours and takes 4 years to complete. For national rankings, see our Best Machine Learning Bachelor's Programs guide.

For graduate study, New York offers 31 master's-level machine learning programs. Leading institutions include Columbia University in the City of New York, Cornell University, Syracuse University. A master's degree can boost earning potential by 15-25%, with senior roles in New York reaching $169,923 or more.

For budget-conscious students, the most affordable machine learning programs in New York are at CUNY Hunter College ($6,930/year), CUNY Queens College ($6,930/year), CUNY Lehman College ($6,930/year). These programs offer strong ROI given New York's competitive tech salaries.

At CUNY Hunter College, a 4-year degree costs approximately $27,720 in tuition alone. With entry-level salaries around $91,497, graduates can typically recoup their investment within 0.5 years.

New York stands as the second-largest hub for machine learning education in the United States, trailing only California. The state's 18 machine learning programs span from world-renowned research universities like Columbia and Cornell to specialized technical institutes like RIT. With over 1,200 ML graduates entering the workforce annually, New York's programs feed talent directly into Wall Street quantitative trading firms, fintech startups, and the growing NYC tech ecosystem.

The concentration of financial services companies in New York creates unique opportunities for machine learning graduates. Goldman Sachs, JPMorgan Chase, and BlackRock actively recruit from Columbia's Computer Science programs and NYU's data science initiatives. Meanwhile, the expanding NYC tech scene, anchored by companies like Spotify, Datadog, and MongoDB, provides additional career pathways beyond traditional finance roles.

Research excellence defines New York's top-tier programs. Columbia University's Fu Foundation School of Engineering publishes over 200 machine learning papers annually, with faculty collaborations spanning Google Research, Meta AI, and IBM Watson. Cornell's computer science department, strengthened by the Cornell Tech campus in Roosevelt Island, bridges academic research with Silicon Valley innovation. These research strengths translate directly into enhanced career prospects for graduates, with 94% of doctoral students securing positions at top-tier technology companies or research institutions.

New York's unique position as both a financial capital and emerging tech hub creates exceptional career opportunities for machine learning graduates. The state's graduates enjoy significantly higher than the national average of $123,000. This premium reflects the high demand for ML talent in New York's competitive job market, where financial services firms compete directly with technology companies for the same skilled professionals.

Wall Street quantitative trading firms represent the highest-paying segment for ML graduates. Renaissance Technologies, Two Sigma, and Citadel actively recruit from Columbia, Cornell, and NYU programs, offering starting packages that can exceed $300,000 for exceptional candidates. These firms apply machine learning to high-frequency trading, risk management, and portfolio optimization, requiring deep mathematical expertise combined with practical programming skills.

The NYC tech ecosystem provides additional opportunities beyond finance. Companies like Datadog (monitoring and analytics), MongoDB (database technology), and Spotify (music recommendation systems) maintain significant engineering presences in New York. These roles typically focus on applied machine learning for product development, offering starting salaries in the $140,000-180,000 range with significant equity upside. For graduates interested in AI/ML engineering careers, New York offers one of the most diverse opportunity sets in the country.

New York institutions offer machine learning education across multiple degree levels and formats. Bachelor's programs provide foundational computer science education with machine learning specialization, typically requiring 4 years and 120-128 credits. These undergraduate programs emphasize mathematical fundamentals, programming proficiency, and core ML algorithms, preparing graduates for entry-level positions or graduate study.

Master's programs represent the most popular path for career advancement, with options including MS in Computer Science with ML concentration, MS in Data Science, and specialized MS in Machine Learning degrees. Columbia, NYU, and Cornell offer intensive 1.5-2 year programs combining theoretical coursework with hands-on projects. These programs attract both recent graduates and working professionals seeking career transitions into ML roles.

Doctoral programs focus on research and academic careers, with average completion times of 5-6 years. PhD students work closely with faculty on cutting-edge research projects, contributing to publications and attending top-tier conferences like NeurIPS, ICML, and ICLR. New York's research universities provide strong doctoral programs, with graduates typically pursuing careers in industry research labs, academia, or founding AI startups.

• Bachelor's Degrees: 4-year programs with ML concentration (8 institutions)
• Master's Degrees: 1.5-2 year intensive programs (12 institutions)
• Doctoral Programs: Research-focused PhD programs (6 institutions)
• Certificate Programs: Professional development for working practitioners (5 institutions)
• Online Programs: Flexible options for remote learning (7 institutions)

Admission to New York's top machine learning programs is highly competitive, with acceptance rates ranging from 8% at Columbia to 25% at mid-tier institutions. Strong mathematical preparation forms the foundation of successful applications, with required coursework typically including calculus through differential equations, linear algebra, statistics, and discrete mathematics. Programming experience in Python, R, or similar languages is increasingly expected, even for bachelor's programs.

For master's programs, competitive applicants typically hold bachelor's degrees in computer science, mathematics, engineering, or related STEM fields with GPAs of 3.5 or higher. The GRE remains required at most institutions, with competitive scores typically above the 80th percentile in quantitative reasoning. Research experience, internships at technology companies, or relevant work experience significantly strengthen applications, particularly for top-tier programs like Columbia and Cornell.

Application deadlines vary by institution and program type. PhD programs typically have December deadlines for fall admission, while master's programs may offer both fall and spring entry with deadlines ranging from December to March. Early application is strongly recommended given the competitive nature of these programs and limited spots available.

• Bachelor's Requirements: High school diploma, strong math/science background, standardized test scores
• Master's Requirements: Bachelor's degree (preferably STEM), GPA 3.5+, GRE scores, programming experience
• Doctoral Requirements: Bachelor's/Master's degree, research experience, strong academic record, faculty recommendations
• International Requirements: TOEFL/IELTS scores, credential evaluation, visa documentation

The demand for flexible machine learning education has driven New York institutions to expand their online and hybrid offerings. Seven universities now provide online machine learning degrees or certificates, allowing working professionals to advance their careers without relocating or leaving current positions. These programs maintain the same academic rigor as on-campus versions while offering evening classes, weekend intensives, and asynchronous coursework options.

Rochester Institute of Technology leads in online ML education with its fully online Master of Science in Data Science program, featuring live virtual labs and industry project partnerships. Syracuse University offers a hybrid Professional Master's program that combines online coursework with quarterly on-campus intensives. These flexible formats have proven particularly popular among professionals working in finance, healthcare, and consulting who seek to integrate ML skills into their current roles.

Online programs maintain strong career outcomes, with graduates achieving median starting salaries within 10% of their on-campus counterparts. The key advantage lies in the ability to continue working while studying, allowing students to immediately apply new skills in their current positions. However, networking opportunities and research experiences may be more limited compared to residential programs.