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We register you for courses each semester, so you can focus on learning instead of logistics.

• Fall 2026 registration begins mid-June

• Once registered, you’ll receive a confirmation and can view your class schedule—including days and times—in .

• Important: You will see a “student hold” in SIO, but don’t worry—this is standard for online graduate certificate students since registration is handled on your behalf.

Carnegie Mellon uses a unit system rather than a credit-hour system.

• 3 units = 1 semester credit hour

• Understanding units helps you better understand the approximate time a course will take you. 

Here’s what this means for you:

• For semester courses, each unit = about 1 hour of work per week 
• For mini courses, each unit = about 2 hours of work per week  

ÎÞÂëרÇø Online courses are not lighter versions of campus offerings—they’re the same world-class, research-backed curriculum taught by ÎÞÂëרÇø faculty. Expect:

• Weekly live-online sessions for active engagement.

• Asynchronous coursework you can complete at your own pace.

• Collaborative learning that emphasizes problem-solving, critical thinking, and immediate application in your workplace.

By design, the programs are challenging yet achievable, preparing you to thrive in your career while balancing life’s demands. 

Our courses are designed for working professionals.

• All live-online classes are held in the evening (ET), paired with flexible asynchronous work so you can balance learning with your personal and professional commitments.

• Your confirmed schedule will appear in after registration.

The AI Engineering - Digital Twins & Analytics Graduate Certificate is a 24 unit program and includes the following two courses taken in order:

FOR Fall 26 START

Please note the following:

• Class will meet live-online twice per week in the evening (ET). In addition, there will be online content to work through on your own time between classes. 
• Course schedule is subject to change.  

Preparatory Resources

To ensure that you are academically prepared for the program, please review the following resources below before the program start: 

See below to learn more about the courses:

Principles of Digital Twins (12 units; 12-830)

Estimated Weekly Time Commitment: ~10-12 hours

This course will introduce you to the concept of digital twins and digital twin modeling. Not only will you learn how to generate and use digital twin models, but you will also learn how to select an appropriate digital twin environment given specific project requirements. 

In addition, you will learn how to build a business case for digital twin adoption, study the role of sensing and information flow within digital twins, and review the role of machine learning in the creation or use of digital twin technology. Finally, you will review the importance of visualization when creating impactful digital twins with different stakeholders and use cases in mind.  

A syllabus overview sample is available here.

Digital Twins and AI for Predictive Analytics (12 units; 12-831)

Estimated Weekly Time Commitment: ~10-12 hours

This course explores the transformative power of digital twins to harness data-driven insights and improve decision making with predictive analytics. You will study topics like data analysis, statistical inference, and applied machine learning to understand the process of collecting, cleaning, interpreting, transforming, exploring and analyzing data generated by digital twin models. 

Using this process, you will learn how to extract pertinent information, communicate insights, and support decision making based on the predictions of how engineered systems might perform under various conditions. The advantages of using visualization techniques to explore data and communicate outcomes will also be highlighted throughout the course. 

A syllabus overview sample is available here.

The AI Engineering Fundamentals Graduate Certificate is a 24-unit program and includes the following two courses that must be taken in order: 

FOR Fall 26 START

Please note the following:

• Class will meet live-online twice per week in the evening (EST). One session will be a lecture/discussion with the faculty while the second is an optional recitation session with the teaching assistant. In addition, there will be online content to work through on your own time between classes. 
• Course schedule is subject to change.  

Preparatory Resources

To ensure that you are academically prepared for the program, please review the following resources below before the program start: 

See below to learn more about the courses:

Machine Learning and Artificial Intelligence for Engineers (12 units; 24-887)

Estimated Weekly Time Commitment: ~10-12 hours

Taught by this course covers fundamental artificial intelligence and machine learning techniques for developing software that is foundational to next generation design and analysis tools. You will study supervised and unsupervised learning, feature engineering, model selection and optimization, dimensionality reduction, ensemble learning, and complete the course with an introduction to deep learning. You will learn the theory behind these techniques, as well as how to efficiently implement them.  This course must be completed prior to taking Deep Learning for Engineers.

A syllabus overview sample is available here.

Deep Learning for Engineers (12 units; 24-888)

Estimated Weekly Time Commitment: ~10-12 hours

Taught by , this course provides the foundations of deep neural networks and their applications to engineering tasks. Taking a hands-on approach, you will apply deep learning to a variety of AI tasks that help solve engineering problems. Topics include convolutional neural networks, recurrent neural networks, long short-term memory, generative adversarial networks, and more.

A syllabus overview sample is available here.

The Foundations of Data Science Graduate Online Certificate is comprised of 30 units and includes the following courses that will be offered as indicated below:

FOR Fall 26 START

• The program operates on a mini-semester system. A mini-semester is half of a full semester (7 weeks). Mini’s are numbered starting in the Fall with the first half of fall as Mini 1, the second half of fall as Mini 2. Then the spring begins with Mini 3 and ends with Mini 4; finally, the summer consists of Mini 5 followed by Mini 6.  

• Class will meet live-online 1-2 times per week (depending on the course). In addition, there will be online content to work through on your own between classes.
• Course schedules are subject to change.  

See below to learn more about these courses:

Probability & Statistics (6 units; 36-640)

Estimated Weekly Time Commitment: ~10-12 hours

Learn how to understand and correctly apply fundamental terminology and techniques in future data analysis situations. Explore the theoretical aspects of probability and statistical inference, including basic probability, random variables, univariate and bivariate probability distributions, statistics, likelihood, point and interval estimation, hypothesis testing, and the frameworks underlying linear and logistic regression and Naive Bayes. Mathematical details are supplemented with computer-based examples and exercises (e.g. visualization and simulation).

A syllabus overview sample is available here. 

Gaining Insights through Statistical Modeling and Machine Learning (6 units; 36-641)

Prerequisite: Probability & Statistics for Data Science
Estimated Weekly Time Commitment: ~10-12 hours

Designed to teach you how to approach and analyze data, topics include data input/output, processing, exploratory analysis, clustering, common regression and classification models (including those of classical statistics and of machine learning), and experimental design. Practice using these methods on real-world data and subsequently apply them when analyzing data in the program's Data Science Capstone course.

A syllabus overview sample is available here.

Telling Impactful Stories with Data Visualization (6 units; 36-642)

Estimated Weekly Time Commitment: ~10-12 hours

Explore the most common forms of graphical displays and their (mis)uses. Learn how to create well-designed graphs and understand them from a statistical perspective, while working with increasingly common, complex data structures (temporal, spatial, and text data). All assignments will be completed in R. Throughout the course, communication skills will play an important role.

A syllabus overview sample is available here.

Introduction to Data Science Computing Workflows (6 units; 36-643)

Estimated Weekly Time Commitment: ~10-12 hours

Learn how to apply computational thinking to data processing and analysis problems through common programming languages (R). Topics include defining and manipulating vectors, lists, and data frames; processing strings and applying regular expressions in string searches; input and output data; writing functions; applying numerical methods such as integration and optimization; working with data-and-time-based data; and applying unit testing.

A syllabus overview sample is available here.

 
Applications of Real World Data Science:  A Capstone Experience (6 units; 36-644)

Prerequisites: The first four courses must be completed prior to taking the Capstone course.  
Estimated Weekly Time Commitment: ~10-12 hours

In the capstone course, work with real-world data to apply the skills and knowledge acquired throughout the program. Supported by subject matter experts, you will have the opportunity to practice synthesizing and communicating results in a clear and concise manner.

A syllabus overview sample is available here.

The Generative AI & Large Language Models Graduate Certificate is a 36 unit program and includes the following courses that will be offered as indicated below:

FOR Fall 26 START

Please note the following:

• Class will be held live-online once or twice per week in the evening (EST). The final schedule with days/times will be available in SIO when you are registered for the courses. In addition, there will be online content to work through on your own time between classes. 
• Course schedules are subject to change.

Preparatory Resources

To ensure that you are academically prepared for the program, please review the following resource below before the program start: 

See below to learn more about the courses:

Large Language Models: Methods and Applications (12 units; 11-967)

Estimated Weekly Time Commitment: ~10-12 hours

This course provides a broad foundation for understanding, working with, and adapting existing tools and technologies in the area of Large Language Models like BERT, T5, GPT, and others.

A syllabus overview sample is available here.

Large Language Model Systems (12 units; 11-968)

Estimated Weekly Time Commitment: ~10-12 hours

LLM's are often very large and require increasingly larger data sets to train, which means developing scalable systems is critical for advancing AI. In this course, you will learn the essential skills for designing and implementing scalable LLM systems.  

A syllabus overview sample is available here.

Multimodal Machine Learning (12 units; 11-977)

Estimated Weekly Time Commitment: ~10-12 hours

In this course, you will learn the fundamental mathematical concepts in machine learning and deep learning that are relevant to the five main challenges in multimodal machine learning: 

1. Multimodal representation learning
2. Translation and mapping
3. Modality alignment
4. Multimodal fusion
5. Co-learning 

The mathematical concepts you will learn include, but are not limited to, multimodal auto-encoder, deep canonical correlation analysis, multi-kernel learning, attention models and multimodal recurrent neural networks.

You will also review recent papers describing state-of-the-art probabilistic models and computational algorithms for multimodal machine learning and discuss the current and upcoming challenges. Finally, you will study recent applications of multimodal machine learning including multimodal affect recognition, image and video captioning and cross-modal multimedia retrieval.

A sample syllabus from a previous section and a course video are pending and will be added when available.

The Machine Learning & Data Science Foundations Graduate Certificate is a 36 unit program and includes the following courses offered in the following schedule:

FOR Fall 26 START

• Note, the program operates on a mini-semester system which is half of a full semester (7 weeks). For example, Python 1 will be delivered in the first 7 weeks of the semester, followed by Python 2 in the second 7 weeks of the semester.  
• Class will be held live-online one time per week in the evening (EST). The final schedule with days/times will be available in SIO when you are registered for the courses. In addition, there will be online content to work through on your own time between classes.  
• Students who have successfully passed the Python for Data Science and/or Math Foundations & Computational Foundations waiver exams will be enrolled in the remaining classes as available. If a course is not available in the upcoming semester, they will begin the first semester an available course occurs. 
• Foundations of Computational Data Science can only be taken after successfully completing or waiving Python for Data Science, Math Foundations & Computational Foundations.  
• Course schedules are subject to change.

Course Waivers

Students are required to complete 36 units of coursework to earn the Machine Learning & Data Science Certificate. However, they may waive up to 12 units of coursework upon successful completion of exemption exam(s):

• Math Waiver Exams (one exam for two courses)
  • Math Fundamentals of Machine Learning (6 units)
  • Computational Fundamentals of Machine Learning (6 units)
• Python Waiver Exams (one exam for two courses)
  • Python for Data Science I (6 units)
  • Python for Data Science II (6 units)

If a student passes the waiver exam for more than 12 units of coursework, they will select which two courses will be waived and be required to complete the remaining coursework in order to earn the certificate.

Waived courses do not carry academic credit, and no tuition will be charged for them. Any scholarships awarded to the waived courses will be forfeited.

Exemption Exam Process

• Students indicate their interest in taking the exam(s) within their program application.
• Exemption exams are available only to students who have submitted their enrollment deposit.
• After the enrollment deposit is received, additional details about scheduling and completing the exam(s) will be provided.

Waiver Exams will take place as follows:

The exams will take place in Canvas, our online course management system. You will have from Monday at 8:00 AM (ET) through Monday at 5:59 AM (ET) to complete the exams. 

• If you indicated interest in taking a waiver exam in your application, you will automatically be contacted in advance of the exam with details. If you did not indicate this, contact your Program Specialist who can add you to the waiver exam. 

See below for more information about the courses.

Mathematical Foundations of Machine Learning (6 units; 11-960)

Estimated Weekly Time Commitment: ~10-12 hours

This course offers the necessary mathematical background to understand machine learning. Topics will include probability, linear algebra, and multivariate differential calculus. Students will also learn how to translate these foundational math skills into concrete coding programs.

A syllabus overview sample is available here.

Computational Foundations for Machine Learning (6 units; 11-961)

Estimated Weekly Time Commitment: ~10-12 hours

This course offers the necessary computational background for studying machine learning. Topics include computational complexity, analysis of algorithms, proof techniques, optimization, dynamic programming, recursion, and data structures.

A syllabus overview sample is available here.

Python for Data Science (delivered in two parts, 6 units each; 11-904 & 11-905)

Estimated Weekly Time Commitment: ~10-12 hours

This course teaches the concepts, techniques, skills, and tools needed for developing programs in Python. Topics include types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations. This course can be waived for computer science professionals who are already fluent in Python. Syllabi overview samples for both Python Part 1 and Python Part 2 will give you a sense of what to expect in each course. 

Foundations of Computational Data Science (12 units; 11-973)

Estimated Weekly Time Commitment: ~10-12 hours

This course offers a hands-on introduction to foundational computational data science concepts, including computing systems, analytics, and human-centered data science. Upon completing the coursework, students will have the necessary skills to obtain further graduate education in data science and/or artificial intelligence.

A syllabus overview sample is available here.

The Managing AI Systems Graduate Certificate includes the following courses that  will be offered on the following schedule: 

FOR Fall 26 START

• Class Meetings - classes will meet live-online bi-weekly (meet in week 1, don't meet in week 2, etc.). In addition, there will be online content to work through on your own time between classes.  
• The course schedule is subject to change.  

See below to learn more about these courses:

Operationalizing AI Systems (94-805)

Estimated Weekly Time Commitment: ~8-10 hours

This course provides an introduction to the practical aspects of managing AI throughout its lifecycle, from scoping to deployment, all while developing a systems-thinking mindset to navigate complexity effectively. 

A syllabus overview sample is available here.

Course Project
Students will demonstrate their management approach for an organizational challenge based on a hypothetical, industry-based scenario. 

Responsible AI (94-809)

Estimated Weekly Time Commitment: ~8-10 hours

This course introduces students to the ethical considerations of AI implementation, covering risk mitigation strategies, test and evaluation methods, and governance frameworks to ensure responsible AI deployment. 

A syllabus overview sample is available here.

Course Project
In this course, students will develop a governance plan for AI implementation based on an industry-based case study.

Building the AI Organization (94-704)

Estimated Weekly Time Commitment: ~8-10 hours

AI systems are complex and require robust planning to implement successfully. In this course, students will learn to formulate AI strategies that align with organizational goals, consider talent acquisition, follow change management best practices, and foster trust between AI systems and stakeholders. 

A syllabus overview sample is available here.

By the end of this course, students should know how to answer questions like:

• How can AI support organizational value propositions?
• What people and skills should be on your AI team?
• How do you build AI literacy in your organization and dispel common AI myths?
• How do you organize AI efforts in your organization?
• How do you prioritize AI projects across business functions?
• How do you stay current on AI trends and build a learning organization?

Course Project

In this course, students will develop an organizational roadmap and experimentation plan that articulates their strategy for AI implementation.