Campus & Online ML Master’s Programs from 50 Universities

What Is a Master’s in Machine Learning?

Machine learning (ML) is a core subfield of artificial intelligence focused on building models that learn from data and improve over time. A master’s degree in machine learning teaches students how to develop these systems through advanced coursework in ML algorithms, statistics, probability, optimization, and software engineering.

Students gain hands-on experience working with large datasets, selecting and tuning models, and turning raw data into predictive tools used in fields like healthcare, finance, manufacturing, cybersecurity, and product development.

ML Master’s Program Curriculum

Most machine learning master’s degrees cover the topics below. Course titles vary by school, but you’ll see versions of these subjects in almost every ML master’s curriculum.

Machine Learning Foundations

Introduces core algorithms like regression, classification, clustering, and decision trees. Students learn how to choose, train, and evaluate ML models for different scenarios.

Probability and Statistics

Covers distributions, statistical modeling, and Bayesian reasoning. These tools help you quantify uncertainty, validate assumptions, and understand model reliability.

Optimization

This part of the program focuses on gradient descent, convex optimization, and related techniques to efficiently train and improve machine learning models.

Deep Learning

Explores neural networks and advanced architectures like CNNs, RNNs, and transformers. Students learn how to train and apply these models to tasks in computer vision, NLP, and multimodal AI.

Algorithms and Data Structures

Covers the computational principles behind scalable ML. Students learn how to design models and pipelines that can handle large datasets, real-time inference, and complex workflows.

Reinforcement Learning

Introduces algorithms that learn through interaction and reward-based feedback. Common applications include robotics, resource allocation, and autonomous decision-making.

Natural Language Processing (NLP)

Covers text processing, embeddings, sequence modeling, and the core concepts of modern language models. Students learn how to build systems that understand, summarize, and generate human language.

Computer Vision

Teaches image and video modeling techniques, including classification, object detection, segmentation, and multimodal approaches that combine visual and language-based learning.

Types of Master’s in ML Programs

Universities structure machine learning master’s degrees in several ways. These are the most common formats:

ML Specializations in Broader Degrees

Machine learning concentrations within computer science, data science, or engineering degrees. Your diploma lists the primary degree (e.g., MSCS) and the ML specialization shows on your transcript. These programs are best for students who want strong ML skills without sacrificing the flexibility and recognition of a traditional STEM credential.

Dedicated Machine Learning Master’s Degrees

An ML-focused curriculum with less unrelated electives. Dedicated ML masters delve deep into algorithms, modeling, optimization, and advanced machine learning techniques. They’re a great fit for students seeking technical roles like machine learning engineer or applied ML researcher.

Professional or Applied ML Master’s Degrees

Programs built for working professionals and career changers that prioritize hands-on skills, popular tools, and portfolio-building over academic research. This format works well for people who want job-ready machine learning skills and a more application-focused education.

Interdisciplinary ML Master’s Degrees

Machine learning programs offered through business, statistics, bioinformatics, robotics, and other departments. These degrees blend ML training with industry-specific coursework, making them ideal for students who want to apply machine learning expertise to a particular industry or technical specialty.

Admissions Requirements for ML Masters

Most machine learning master’s programs look for applicants with:

Strong programming skills, especially in Python.

Math background in calculus, linear algebra, and probability.

Basic knowledge of CS topics like algorithms and data structures.

Hands-on experience with statistics or data analysis.

Students from computer science, engineering, mathematics, physics, and other STEM fields typically transition well into ML master’s programs. Applicants without this background may need to complete prerequisite courses before being admitted.

Career Paths for ML Master’s Graduates

Machine Learning Engineers

ML Engineers design, train, and deploy models that learn from data. They build systems that power predictions, recommendations, personalization, and automated decision-making.

Data Scientists

Data Scientists analyze large datasets to uncover patterns and insights that guide business decisions. They use machine learning to predict outcomes, segment users, detect anomalies, and optimize processes.

Natural Language Processing Engineers

NLP Engineers build systems that understand and generate human language. They use machine learning to create chatbots, language models, document classifiers, semantic search, and text analysis tools.

Computer Vision Engineers

Computer Vision Engineers develop models that interpret images and video. They apply ML to object detection, image recognition, autonomous navigation, quality inspection, and medical imaging.

Robotics Engineers

Robotics engineers design robots that perceive, navigate, and act autonomously. Machine learning helps robots interpret environments, plan movements, adapt to conditions, and get better with experience.

ML Infrastructure Engineers

ML Infrastructure Engineers build the platforms, pipelines, and tools that support large-scale machine learning operations. They ensure models are trained, deployed, monitored, and scaled efficiently.

AI Product Developers

AI Product Developers create software features and apps powered by machine learning. They integrate ML models into products to deliver personalization, predictive insights, and intelligent functionality.

ML Master’s Program Directory

We identified 50 accredited universities offering 54 machine learning master’s programs across 25 states. Only programs with a dedicated ML curriculum or a clearly defined ML major are listed. Programs are listed alphabetically by state.

Arizona

The University of Arizona

MS in Information Science: Machine Learning

Format: Online or Hybrid

Location: Tucson, AZ

Machine Learning Master’s Programs

What Is a Master’s in Machine Learning?

ML Master’s Program Curriculum

Machine Learning Foundations

Probability and Statistics

Optimization

Deep Learning

Algorithms and Data Structures

Reinforcement Learning

Natural Language Processing (NLP)

Computer Vision

Types of Master’s in ML Programs

ML Specializations in Broader Degrees

Dedicated Machine Learning Master’s Degrees

Professional or Applied ML Master’s Degrees

Interdisciplinary ML Master’s Degrees

Admissions Requirements for ML Masters

Career Paths for ML Master’s Graduates

Machine Learning Engineers

Data Scientists

Natural Language Processing Engineers

Computer Vision Engineers

Robotics Engineers

ML Infrastructure Engineers

AI Product Developers

ML Master’s Program Directory

Arizona

The University of Arizona

MS in Information Science: Machine Learning

Arkansas

Arkansas State University

MS in Information Science: Machine Learning

California

California State University, East Bay

MS in Computer Science – AI & Machine Learning Concentration

San Jose State University

MS in Statistics with Machine Learning Specialization

Santa Clara University

M.S. in Electrical & Computer Engineering – Signal Processing & Machine Learning Focus Area

University of California, San Diego

MS in Electrical & Computer Engineering – Machine Learning & Data Science

University of Southern California

MS in Electrical & Computer Engineering – Machine Learning & Data Science Emphasis

Colorado

Colorado State University Global

Online Master’s in Artificial Intelligence (AI) & Machine Learning

District of Columbia

George Washington University

Online Master of Engineering in AI and Machine Learning

Howard University

Master’s in Computer Science (MCS) with Algorithms & Machine Learning Specialization

Georgia

Georgia Institute of Technology

Online Master of Science in Computer Science (OMSCS) with ML Specialization

Georgia Southern University

MS in Computer Science with Machine Learning Specialization

Georgia State University

Data Science & Analytics MSA with concentration in Big Data & Machine Learning

Illinois

Northwestern University

Master of Science in Machine Learning and Data Science

Indiana

Purdue University

MS in Artificial Intelligence with AI & Machine Learning Major

Louisiana

Tulane University

Online MSCS with Artificial Intelligence & Machine Learning Focus Area

Maryland

University of Maryland, College Park

Master of Science in Applied Machine Learning

Maine

Northeastern University (Roux Institute)

MPS in Applied Machine Intelligence

Massachusetts

Northeastern University

MSECE with Concentration in Computer Vision, Machine Learning & Algorithms

University of Massachusetts Amherst

MS in Electrical & Computer Engineering with AI & Machine Learning Systems Concentration

Michigan

University of Michigan, Ann Arbor