University of Colorado Boulder Master’s in Artificial Intelligence

Program Overview

College of Engineering and Applied Science

The University of Colorado Boulder offers a Professional Master’s in Artificial Intelligence launching in fall 2026, designed specifically for engineers, applied scientists, and technical professionals seeking career advancement in AI leadership roles. This comprehensive 30-credit graduate program must be completed within four years and follows a course-based structure without thesis requirements. The program targets working professionals looking to transition into or advance within the rapidly growing artificial intelligence field.

Academic Requirements and Admissions

  • Maintain a minimum 3.0 GPA.
  • Earn grades of B or better in ethics and breadth courses.
  • On-campus applicants follow the traditional admission process.
  • Coursera applicants use performance-based admissions: complete pathway courses with B grades.
  • The program welcomes diverse backgrounds but expects:
    • Strong foundation in computer science, applied mathematics, or engineering
    • Programming experience
    • College-level understanding of calculus, linear algebra, discrete math, and statistics

Flexible Learning Options

Students can pursue this degree through two distinct pathways:

  • traditional on-campus study at CU Boulder
  • fully online through the innovative Coursera platform.

The online MS-AI option provides identical credentials to on-campus students with no “online” designations on official transcripts or diplomas.

Both programs offer the same rigorous curriculum taught by CU Boulder’s highly regarded faculty, ensuring academic quality regardless of delivery method.

Curriculum and Machine Learning Focus

The curriculum addresses core AI engineering expertise including machine learning, statistical learning, data mining, and ethics through carefully structured requirements.

Students complete one ethics course, four breadth requirement courses (including two Foundations of AI and two AI Core courses), three electives, and two interdisciplinary courses.

Machine learning represents a central component, with dedicated courses in probabilistic models, neural networks, deep learning, computer vision, and natural language processing available as core options.

Required

CSCI 5XXX/INFO 5XXX: Ethics of AI (required)

Foundations

Choose two of the following:

CSCI 5434 (3) Probability for Computer Science
This course introduces probability and statistics from an algorithmic perspective, using computer science examples to teach foundational concepts that support advanced CS coursework.

CSCI 5254: Convex Optimization and Its Applications
Students learn convex analysis, optimization theory (linear, quadratic, semidefinite, geometric), and key algorithms (descent, interior-point), then apply these tools in signal processing, machine learning, control, and other engineering domains.

CSCI 5444 (3) Introduction to Theory of Computation
This course reviews regular expressions and finite automata, explores Turing machines and the Chomsky hierarchy, and examines context-free grammars, push-down automata, and fundamental computability results.

CSCI 5535 (3) Fundamental Concepts of Programming Languages
Students study common language features, formal and informal description methods, and implementation techniques to build insight into how languages interact with underlying machine architectures.

CSCI 5622 (3) Machine Learning
This course covers supervised, reinforcement, and unsupervised learning with practical and theoretical focus on neural networks, decision trees, support vector machines, and Q-learning, connecting these methods to data mining and statistical modeling.

CSCI 5654 (3) Linear Programming
Students explore the simplex method and its variants, duality theory and complementary slackness, network flow algorithms, and receive an introduction to integer programming.

CSCI 5646 (3) Numerical Linear Algebra
This course presents direct and iterative solutions to linear systems, eigenvalue and eigenvector computations, error analysis, and orthogonal reduction techniques, requiring strong linear algebra and programming skills.

CSCI 5822 (3) Probabilistic and Causal Modeling in Computer Science
Students learn graphical models, Bayesian analysis, and multivariate statistics for probabilistic and causal inference, applying these techniques to interpret large data sets in healthcare, economics, marketing, and social sciences.

CSCI 5854 (3) Theoretical Foundations of Autonomous Systems
This course covers modeling of timed, differential, switched, and hybrid dynamical systems, reachability and stability verification, temporal logic specifications, and controller synthesis for applications in automotive, robotics, and medical devices.

AI Core

Choose two from the following:

CSCI 5135 (3) Computer-Aided Verification
This course covers two-level and multilevel minimization, optimization via expert systems, algebraic and Boolean decomposition, layout methodologies, state assignment, encoding and minimization, and silicon compilation.

CSCI 5302 (3) Advanced Robotics
Students investigate current research in robotics and gain hands-on experience by tackling a grand-challenge project that integrates sensing, planning, and control.

CSCI 5502 (3) Data Mining
Introduces techniques for discovering patterns in large data sets, including data preprocessing, warehousing, association rule mining, classification, clustering, and specialized methods for time-series, social networks, multimedia, and web data.

CSCI 5722 (3) Computer Vision
Explores image-based algorithms for real-world inference, covering imaging models and calibration, early vision (filters, edges, texture, stereo, optical flow), mid-level vision (segmentation, tracking), vision-based control, and object recognition.

CSCI 5832 (3) Natural Language Processing
Examines theoretical and practical challenges in getting computers to understand and generate human language, covering language phenomena analysis and the construction of practical NLP programs.

CSCI 5839 (3) User-Centered Design and Development
Teaches user-centered methods for requirements analysis, design, and evaluation of software applications, emphasizing iterative testing, user feedback, and usability principles.

CSCI 5922 (3) Neural Networks and Deep Learning
Introduces neural network architectures and deep learning methods, focusing on training algorithms, model evaluation, and practical implementation for tasks such as classification and regression.

CSCI 7000 (1–4) Current Topics in Computer Science
Offers variable-credit study of emerging research areas in computer science outside standard subfields, allowing students to explore cutting-edge developments.

INFO 5612 (3) Recommender Systems
Covers collaborative, content-based, knowledge-based, and hybrid recommendation methods; examines applications in e-commerce, music, social media, and online advertising; and addresses controversies like filter bubbles and algorithmic bias.

More curriculum information can be found here: https://catalog.colorado.edu/courses-a-z/csci/

Cost

The on-campus program costs nearly double the online Coursera version:

  • Online: $15,750 total
  • On-campus: ~$30,745 total

The on-campus program also includes mandatory fees ($1,781 total) that the online program doesn’t have, plus potential additional costs for health insurance and other campus services.

Online Cost Breakdown

Total Program Cost: $15,750 for the complete 30-credit Master’s degree

Per Credit Cost: $525 per credit hour (same rate for in-state, out-of-state, and international students)

Key Cost Features

  • Pay-as-you-go structure: You only pay for courses as you take them, not upfront for the entire program
  • No student fees: Unlike traditional on-campus programs, there are no additional mandatory fees
  • Flat rate pricing: No difference in cost based on residency status – everyone pays the same rate
  • Flexible payment: You can upgrade to the for-credit experience during any enrollment window

More tuition details can be found here:

On-Campus Program Cost Breakdown

Computer Science falls under “Arts & Sciences & All Other” category:

  • Per semester tuition (9+ credits): $7,241 (flat rate for 9 or more credits)
  • Mandatory fees per semester: $445.33 (for full-time students taking more than 1 class/5+ hours)
  • Total per semester: $7,686.33

Total Program Cost Estimate

For a 30-credit program completed over 2 years (4 semesters):

  • Total tuition: $28,964 (4 semesters × $7,241)
  • Total fees: $1,781.32 (4 semesters × $445.33)
  • Grand total: $30,745.32

Additional Costs

  • New Student Fee: $62 (one-time)
  • Health Insurance: $2,442 per year (if needed)

More details can be found here: https://www.colorado.edu/bursar/media/1058

Career Outcomes and

This professional master’s degree prepares graduates for advanced technical leadership roles in AI engineering, with emphasis on practical applications rather than research. The program produces workforce-ready graduates equipped with versatile specialized skills suitable for industry positions.

Graduates gain expertise in cutting-edge AI technologies, ethical considerations, and interdisciplinary applications that enhance their career advancement prospects in the expanding artificial intelligence sector.

Professional Development

Based on the program’s professional focus and curriculum, here are the top 5 job positions this MS-AI program prepares you for:

  1. Machine Learning Engineer – Building and deploying ML models in production environments, directly supported by the hands-on machine learning curriculum.
  2. AI/ML Engineering Manager – Leading technical teams developing AI products and solutions, combining the program’s technical expertise with leadership preparation.
  3. AI Solutions Architect – Designing enterprise AI implementations for clients, requiring the broad technical knowledge and interdisciplinary skills the program provides.
  4. Computer Vision Engineer – Developing image recognition and visual AI systems using the specialized computer vision courses offered in the curriculum.
  5. AI Product Manager – Managing AI-driven products and features, leveraging the program’s combination of technical depth, ethics training, and business applications.

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