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The Master of Science in Machine Learning (MS in ML) at Carnegie Mellon University is a highly selective, research-oriented, STEM-designated graduate program that offers rigorous training in the theory and application of machine learning. Administered by the Machine Learning Department (MLD) in CMU's School of Computer Science, the program is designed for students with a strong foundation in mathematics and computer science who aim to pursue cutting-edge research or leadership roles in AI, data science, and computational intelligence.
This program is distinct from applied or systems-based data science programs—it emphasizes mathematical rigor, algorithmic innovation, and theoretical depth, making it one of the most academically intensive ML master's programs in the world.
Program Format & Duration
Full-time, residential program
Duration: 2 years (4 semesters)
STEM-designated (OPT eligible for international students)
Fall intake only
GRE recommended (especially for applicants without a CMU undergraduate degree)
Students typically write a Master’s thesis or complete a project portfolio
Core Curriculum Components
The MS in ML curriculum provides students with a strong foundation in the mathematics, algorithms, and practical tools of machine learning, while offering flexibility to pursue advanced electives and research specializations.
Core Coursework
All students complete foundational courses in:
Mathematical Foundations
Probability and Statistics
Convex Optimization
Linear Algebra for ML
Core Machine Learning
Machine Learning Theory
Deep Learning
Probabilistic Graphical Models
Kernel Methods
Statistical Machine Learning
Computer Science Foundations
Algorithms and Data Structures
Programming in Python/C++
Software Engineering for ML
Ethics and Societal Impact
Responsible AI and Algorithmic Fairness
Interpretability and Explainability in ML
Data Privacy and Security
Electives
Students can customize their track through advanced electives in:
Reinforcement Learning
Natural Language Processing
Computer Vision
Generative Models
Causal Inference
Robotics and Sensing
Computational Neuroscience
Meta-Learning and AutoML
Research/Project Requirement
Students must complete a thesis or project portfolio
Typically conducted under the supervision of MLD faculty or affiliated labs
Topics range from theoretical contributions to applied ML in vision, healthcare, finance, or policy
While deeply academic, the MS in ML program includes substantial hands-on components through coursework, research labs, and collaboration with industry and faculty-led initiatives.
Research Opportunities
Students are encouraged to engage with world-class labs, including:
Machine Learning Department (MLD)
Carnegie Mellon AI (CMU AI)
Language Technologies Institute (LTI)
Robotics Institute (RI)
Auton Lab, Causal AI Lab, and others
Collaborative Projects
Students often publish in top conferences (NeurIPS, ICML, CVPR)
Participate in CMU-led initiatives involving real-world problems in healthcare, robotics, education, and climate
Projects may be industry-sponsored or integrated into PhD lab ecosystems
Seminars and Colloquia
Weekly ML Department seminars with global leaders in machine learning
Workshops on fairness, interpretability, and security in ML
AI-focused hackathons and research challenges
Graduates of the MS in Machine Learning at CMU are recognized globally for their theoretical depth, research readiness, and leadership potential in AI. Many continue to PhD programs, while others transition directly into advanced R&D, applied science, or technical leadership roles.
Career Outcomes
Alumni often secure roles such as:
Top Employers & Institutions
Academic Pathways
Salary Overview-
Top Employers-
Geographic Placement-
Industry Sectors-
Career Growth and Progression-
What This Means for You-
Bachelor’s Degree & GPA: Hold a bachelor’s degree (typically in CS, engineering, math, or related), with a minimum GPA of 3.0 based on your raw university transcript Transcripts: Upload unofficial transcripts from all postsecondary institutions when you apply; official versions and certified English translations are required only if admitted GRE Scores: Submit GRE General Test scores (less than 5 years old) unless applying to a program that explicitly waives it (e.g., MCDS waives for CMU alumni)English Proficiency: International applicants on F‑1/J‑1 visas must submit valid TOEFL (iBT ≥100), IELTS (≥7), or Duolingo scores, with no waivers, and scores within the acceptable age windowStatement of Purpose: Provide a concise essay (typically 1–2 pages) addressing your background, interests/goals, and suitability for your chosen SCS program.Resume/CV: Include a current résumé or CV detailing your academic record, research/work experience, technical skills, publications, and honors .Program-Specific Materials: Some master’s programs (e.g., MCDS, MSAII, MIIS) also require a short intro video or supplementary statementsApplication Fee and Deadlines: Pay the per-program fee ($80 early / $100 standard) and submit all materials by the SCS-wide deadlines—early deadline: Nov 19, 2025 (3 p.m. EST); final deadline: Dec 10, 2025 (3 p.m. EST)Program-specific Requirements: Check individual department pages—some may recommend GRE Subject Test, have additional prerequisites, or have their own deadlines and requirements Three‑Year Bachelor’s Degrees: CMU requires a U.S.-equivalent bachelor’s degree, so applicants with three-year degrees must confirm equivalency with admissions, as they are not automatically accepted .WES Evaluation: Official academic transcripts must be submitted directly; WES or other credential evaluations are not accepted in place of these records.Data AnalystData ScientistBusiness AnalystStatisticianStatistical ProgrammerSAS ProgrammerMachine Learning EngineerDeep Learning EngineerAI ResearcherData EngineerBI DeveloperData ArchitectAnalytics ConsultantAdvisory ConsultantQuantitative AnalystRisk AnalystOperations AnalystMarketing AnalystResearch ScientistInsights AnalystForecasting AnalystHealthcare Data AnalystFinancial Data AnalystProduct Analyst
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Carnegie Mellon University
