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Generative AI & Large Language Models

Online Graduate Certificate

Taught by

School of Computer Science Faculty

Program Length

12 months

Next Start Date

Fall 2026

Application Deadline

Priority*: March 4, 2026
Final: July 2026

*All applicants who submit by the priority deadline will receive a partial scholarship award. 

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Who is this Program For?

This program is for experienced computer scientists and engineers with a strong background in computer science and machine learning who are ready to move beyond surface-level use of generative AI and engage with its underlying models, theory, and systems.

What You Will Learn

By the end of this graduate certificate program, you will have the skills to understand how generative AI systems work, adapt and evaluate large language models, integrate multimodal data, and design scalable, efficient AI systems that translate cutting-edge research into real-world impact.

The CMU Difference

Learn from the School of Computer Science faculty who are shaping the field of artificial intelligence. This online experience pairs with CMU's research-driven instruction with intentional course design, ensuring complex generative concepts are taught with the same rigor and depth as on-campus study.  

Class Profile

INDUSTRIES REPRESENTED

Industries represented in the Generative AI & LLM program

INDUSTRIES REPRESENTED

pie chart of industries represented in the GAI and LLM program

AVERAGE AGE

Average age of students in the Generative AI & LLM Program
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Curriculum

This curriculum delivers rigorous, systems-level training in generative AI and large language models. You’ll move beyond tool usage to study the mathematical, algorithmic, and architectural foundations behind modern models, working hands-on with transformer-based systems such as BERT, T5, and GPT. 

Through coursework in multimodal learning and large language model systems, you’ll learn to evaluate performance tradeoffs, address scalability and alignment challenges, and deploy models at production scale—preparing you to design, analyze, and implement sophisticated AI systems with confidence and rigor.

Course Descriptions

Course Number: 11-967

Number of Units: 12 units

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.

Throughout this course, you will learn:

  • A range of topics including systems, data, data filtering, training objectives, RLHF/instruction tuning, ethics, policy, evaluation, and other human-facing issues.
  • How transformer architectures work and why they are better than LSTM-based seq2seq models. You’ll explore decoding strategies and techniques for pretraining, attention, prompting, and more through readings and hands-on assignments.
  • How to apply the skills you’ve learned in a semester-long course project, using locally sourced model instances that allow you to go beyond commercial APIs.
  • How to compare and contrast different models in the LLM ecosystem to determine the best model for a given task.
  • How to implement and train a neural language model from scratch using PyTorch.
  • How to utilize open source libraries to fine-tune and perform inference with popular pre-trained language models.
  • How to apply LLMs in downstream applications and understand how pre-training decisions affect task suitability.
  • How to design new methodologies that leverage existing large-scale language models in novel ways.

Please note: In order to complete homework and activities, students will need to sign up for Amazon Web Services (AWS) or an equivalent service that offers access to A10g or similar GPUs. The AWS cost to complete assignments will range from $150–$300, depending on usage. Additionally, students will need to sign up for the OpenAI API. The cost to complete the assignments via OpenAI will be up to $25. Instructions for accessing both services will be provided at the start of the course.

Course Number: 11-977

Number of Units: 12 units

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:

  • Multimodal representation learning
  • Translation and mapping
  • Modality alignment
  • Multimodal fusion
  • Co-learning

The mathematical concepts you will learn include, but are not limited to, multimodal auto-encoders, 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 current and emerging challenges. Finally, you will study recent applications of multimodal machine learning including multimodal affect recognition, image and video captioning, and cross-modal multimedia retrieval.

Please note: The Multimodal Machine Learning course may require Amazon Web Services (AWS) and/or OpenAI or other services to complete assignments, with fees up to $300 (subject to change). More details will be available as you get closer to the course start date.

Course Number: 11-968

Number of Units: 12 units

LLMs are often very large and require increasingly larger datasets 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.

Throughout the course, you will:

  • Learn the approaches for training, serving, fine-tuning, and evaluating LLMs from the systems perspective.
  • Gain familiarity with sophisticated engineering using modern hardware and software stacks needed to accommodate the scale.
  • Acquire essential skills for designing and implementing LLM systems, including:
    • Algorithms and system techniques to efficiently train LLMs with huge data
    • Efficient embedding storage and retrieval
    • Data-efficient fine-tuning
    • Communication-efficient algorithms
    • Efficient implementation of reinforcement learning with human feedback
    • Acceleration on GPU and other hardware
    • Model compression for deployment
    • Online maintenance
  • Learn about the latest advances in LLM systems regarding machine learning, natural language processing, and systems research.

Please note: The Large Language Model Systems course may require Amazon Web Services (AWS) and/or OpenAI or other services to complete assignments, with fees up to $300 (subject to change). More details will be available as you get closer to the course start date.

World-Class Faculty

School of Computer Science

The online graduate certificate is taught by Carnegie Mellon University School of Computer Science faculty, home to world-renowned experts in artificial intelligence and machine learning. SCS faculty are pioneers in generative AI and large language model research, bringing cutting-edge advances directly into the classroom through hands-on, application-driven coursework so you develop skills you can apply immediately in professional settings.

Dr. Carolyn Rose

Dr. Carolyn Rosé

Professor, Language Technologies & Human-Computer Interaction

Dr. Daphne Ippolito

Dr. Daphne Ippolito

Assistant Professor, Language Technologies

Dr. Lei Li

Dr. Lei Li

Assistant Professor, Language Technologies

Dr. Louis Philippe Morency

Dr. Louis-Philippe Morency

Associate Professor, Language Technologies

Dr. Yonatan Bisk

Dr. Yonatan Bisk

Assistant Professor, Language Technologies

Dr. Daniel Fried

Dr. Daniel Fried

Assistant Professor, Language Technologies

Dr. Graham Neubig

Dr. Graham Neubig

Associate Professor, Language Technologies & Machine Learning

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Application Requirements

This online graduate certificate program is highly competitive and offers a rigorous curriculum. As a result, Carnegie Mellon has high expectations of its applicants. Our most successful applicants have:

  • A bachelor’s degree in computer science or a related field (machine learning, data science, software engineering)
     
  • Proficient academic history in mathematics, including a minimum of three advanced courses:
    • Calculus, Differential Equations, and Linear Algebra
    • Matrix Math, Nonlinear Programming, and Nonlinear Optimization
    • Probability Theory and Statistics, Probability and Stochastic Processes
    • Combinatorics, Set Theory, and Graph Theory
       
  • Successful academic history in computer science, including a minimum of three courses: 
    • Programming in C, C++, Java, Python
    • Analysis of Algorithms, Data Structures, and Algorithms
    • Computational Complexity
    • Discrete Structures / Discrete Math / Logic
    • Formal Languages
    • Parallel and Distributed Computing
    •  Programming Languages
    • Object-Oriented Programming
    • Operating Systems
    • Software Engineering
       
  • Strong academic history in machine learning, including at least one formal course. Applicants without this experience may be asked to take a machine learning course before enrolling.
     
  • Robust academic or relevant experience in multiple programming languages (C/C++/C#, Java, Swift, Go, Scala, or Rust. Applicants without this experience may be asked to take a  preparatory course before enrolling.
     
  • Dynamic link library experience with PyTorch or TensorFlow. Applicants without this experience may be asked to take a preparatory course before enrolling.

Tuition

Below is a tuition breakdown for the 2025/2026 academic year:

CourseUnitsInvestment
Spring 2026
Multimodal Machine Learning12$8,484
Fall 2026
Large Language Models: Methods and Applications12$8,484
Spring 2027
Large Language Model Systems12$8,484
Total Investment$25,452

By enrolling in our graduate-level program, you'll be investing in your professional growth to prepare for the next wave of Generative AI solutions. We know this is a significant investment — not just for you, but for your family as well.

Carnegie Mellon alumni are eligible for a scholarship to the Graduate Certificate in Generative AI & Large Language Models worth up to 20% of tuition. Indicate your alumni status within the application to be eligible.

Additional Course-Related Expenses

  • Large Language Models: Methods and Applications – To complete homework and activities, students will need to sign up for Amazon Web Services (AWS) or a similar service offering access to A10g (or equivalent) GPUs. Estimated cost: $150–$300 depending on usage. Additionally, students must sign up for the OpenAI API, with assignment costs up to $25. Access instructions will be provided at course start.
  • Multimodal Machine Learning and Large Language Model Systems – AWS and/or OpenAI or similar services may be required, with estimated fees up to $300 per course. More details will be shared closer to the course start dates.

University Fees

A technology fee of approximately $245 per semester will be assessed (subject to change).

Note: The rates listed above apply to the 2025/2026 academic year only. If the program is not completed within this timeframe, tuition may increase slightly for the following academic year.

Students pursuing a graduate certificate are not eligible to receive federal financial aid. However, private loans are a viable alternative to consider with competitive interest rates and borrower benefits. See FastChoice, a free loan comparison service to easily research options.

Using your company's tuition reimbursement benefits can help significantly offset the cost of tuition, especially if you start in the Fall and your tuition benefits restart every calendar year.   

Tuition Benefits in Action

Meet Casey - she plans to start the program in the Fall and was awarded a $1,000 scholarship per course. Her company provides $5,000 in tuition reimbursement per calendar year. Here’s an example of what her tuition breakdown could look like with tuition reimbursement benefits:

FALL
DescriptionTuition or Fee
Course: Large Language Model Systems$8,484
     Less Scholarship-$500
Technology Fee$240
Cloud Computing & AI Tools Fee$300
Tuition Reimbursement-$5,000
Remaining Tuition & Fees Due$3,524
SPRING
DescriptionTuition or Fee
Course: Multimodal Machine Learning$8,484
     Less Scholarship-$500
Technology Fee$240
Cloud Computing & AI Tools Fee$300
Tuition Reimbursement-$5,000
Remaining Tuition & Fees Due$3,524
FALL
DescriptionTuition or Fee
Course: Large Language Models - Methods & Applications$8,484
     Less Scholarship-$500
Technology Fee$240
Cloud Computing & AI Tools Fee$325
Remaining Tuition & Fees Due$8,549

As you can see, Casey is maximizing her tuition reimbursement benefits across two calendar years, resulting in a personal contribution of $15,597. She can also use the payment plan to spread the cost out even further.

Make a Case To Your Employer

If your employer is hesitant about supporting the program, be sure to highlight the value and benefits of completing an online certificate at Carnegie Mellon. You can share that our program: 

  • Consists of three transcripted, credit-bearing courses (not just continuing education units) taught by expert Carnegie Mellon professors from the nationally-ranked School of Computer Science.
  • Teaches you the latest advances in large language model systems, machine learning, natural language processing, and system research.
  • Prepares you to design and implement scalable systems for large language models and teaches you how to efficiently train them with huge data sets, which is critical for advancing AI.
  • Trains you to determine the best model for a given task by evaluating the pros and cons of different models.
  • Empowers you to solve sophisticated engineering problems with modern hardware and software stacks that can accommodate the scale of large language models.
  • Is delivered completely online, which means you can take classes on your own time while maintaining your normal work schedule.

Not sure how to approach your employer? Need specific documents to proceed with enrollment? Contact a Program Specialist for assistance. If you’re ready to make more data-informed decisions, we’re here to help you make that a reality.

CMU provides a monthly payment option, managed by Nelnet Campus Commerce, designed to help students spread out tuition payments into manageable monthly installments. This plan also offers the ease of online enrollment. Should you be admitted and choose to join us, we recommend registering for this plan early to fully benefit from the range of payment options available.

Payment Plan in Action

Meet Jesse - he plans to start the program in the Fall and was awarded a $1,000 scholarship per course. Here's an example of what his tuition breakdown might look like with a payment plan:

FALL
DescriptionTuition or Fee
Course: Large Language Model Systems$8,484
     Less Scholarship-$500
Technology Fee$240
Cloud Computing & AI Tools Fee$300
Payment Plan Enrollment Fee$45
Remaining Tuition & Fees Due$8,569
20% Down Payment - Due Aug. 20$1,713.80
Payment #1 - Due Sept. 1$1,713.80
Payment #2 - Due Oct. 1$1,713.80
Payment #3 - Due Nov. 1$1,713.80
Payment #4 - Due Dec. 1$1,713.80

By taking advantage of the monthly payment plan option, Jesse can break tuition into smaller installments that he can pay throughout the semester. 

Please note: Nelnet payment plans do not carry over from one semester to the next. Therefore, students must re-enroll and establish a new payment plan at the beginning of each semester. 

The Graduate Certificate in Generative AI & Large Language Models is eligible for CMU tuition remission. Review the CMU tuition remission policy to check your eligibility.

Carnegie Mellon University provides services to veterans and their dependents who are eligible for Veterans Education Benefits under the Montgomery G.I. Bill®, Post-9/11 G.I. Bill, and Vocational Rehabilitation and Employment Program. Please note, our online graduate certificates are not currently eligible for the Yellow Ribbon program. 

The process starts with an application directly to Veterans Affairs and once approved, you will provide your Certificate of Eligibility to the Carnegie Mellon Veterans Affairs Coordinator. Contact Information and additional details about the process can be found here.

Students eligible for GI Bill funding may receive scholarship awards prior to full GI Bill funding confirmation. Scholarship awards will be adjusted to reflect GI Bill funding and cannot exceed the cost of tuition/fees.

As part of a global university with locations and students from around the world, the Online Education Unit at Carnege Mellon University welcomes the diverse perspectives that international students bring to our programs.

To help ensure you are fully prepared for the admissions process and, if admitted, for success as a student, this section provides detailed information about requirements for international applicants.

We look forward to reviewing your application.

The Online Education Unit at Carnege Mellon University considers for admission international applicants who reside within, or outside of, the domestic United States. International applicants who reside within or outside of the domestic United States are advised of the following information and additional requirements for international applicants to the program.

Student Visas

Since this program is fully online, enrollment in this program will not qualify students for any type of visa to enter or remain in the United States for any purpose.

Time and Attendance Requirement  

Classes for the program will be taught on the U.S. Eastern Time zone schedule, and students must be available to attend all live classes, regardless of location.

U.S. Sanctions; U.S. Sanctioned Countries

Individuals who are the target of U.S. sanctions or who are ordinarily resident in a U.S. sanctioned country or who live or expect to live in a U.S. sanctioned country while participating in the program are not eligible for admission to this program due to legal restrictions/prohibitions and should not apply. U.S sanctioned countries are currently Belarus, Cuba, Iran, North Korea, Russia, Syria and the following regions of Ukraine: Crimea, Donetsk and Luhansk. In addition, all or a portion of this program may not be available to individuals who are ordinarily resident of certain countries due to legal restrictions.  

Applications received from these individuals will not be accepted. As well, if an individual is admitted to the program and subsequently the individual becomes the target of U.S. sanctions, ordinarily resident of a U.S. sanctioned country or lives in a U.S. sanctioned country while participating in the program (or otherwise becomes ordinarily resident of country in which the program is not available due to legal restrictions), the individual’s continued enrollment in the program may be terminated and/or restricted (due to U.S. legal restrictions/prohibitions) and the individual may not be able to complete the program.  

Licensure in Various Jurisdictions

From time to time Carnegie Mellon reviews the licensing requirements of various jurisdictions in order to assess whether Carnegie Mellon may be precluded from making the program available to applicants that are residents of one or more of these jurisdictions prior to Carnegie Mellon obtaining the relevant license(s). Affected applicants from these jurisdictions, if any, will be notified prior to enrollment if Carnegie Mellon determines that it is unable to make the program available to them for this reason.

Value Added Tax (VAT) and Other Taxes

The tuition, required fees and other amounts quoted for this program do not include charges for applicable Taxes (hereinafter defined). The student is responsible for payment of all applicable Taxes (if any) relating to the tuition, required fees and other amounts required to be paid to Carnegie Mellon for the program, including any Taxes payable as a result of the student’s payment of such Taxes.

Further, the student must timely make all payments due to Carnegie Mellon without deduction for Taxes, unless the deduction is required by law. If the student is required under applicable law to withhold Taxes from any payment due to Carnegie Mellon, the student is responsible for timely (i) paying to Carnegie Mellon such additional amounts as are necessary so that Carnegie Mellon receives the full amount that it would have received absent such withholding, and (ii) providing to Carnegie Mellon all documentation, if any, necessary to permit the student and/or Carnegie Mellon to claim the application of available tax treaty benefits (for Carnegie Mellon review and completion, if warranted and acceptable).

Taxes mean any taxes, governmental charges, duties, or similar additions or deductions of any kind, including all use, income, goods and services, value added, excise and withholding taxes assessed by or payable in the student’s country of residence and/or country of payment (but does not include any U.S. federal, state or local taxes).

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An Online Format, Built for Working Professionals

Mastering generative AI and large language model systems is mathematically rigorous, computationally intensive, and intellectually demanding—and it requires a learning environment that supports both depth and flexibility. The Generative AI and Large Language Models Graduate Certificate is a 12-month online program structured across three semesters, enabling you to engage deeply with advanced material while balancing professional and personal commitments.

Despite its online format, the program upholds the same academic rigor as Carnegie Mellon’s on-campus offerings and demands sustained analytical engagement. You can expect a challenging, graduate-level experience that reflects the School of Computer Science’s leadership in artificial intelligence, machine learning, and systems research.

Live classes meet once per week in a 90-minute session, scheduled in the late afternoons and evenings (EST), and bring together CMU faculty and a cohort of experienced peers for in-depth technical discussion, model analysis, and collaborative problem-solving.

Asynchronous coursework—including advanced readings, technical lectures, and hands-on implementation—allows you to work through complex concepts and experiments on your own schedule, while ongoing faculty engagement ensures you remain challenged, supported, and intellectually connected throughout the program.

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Start Your Application

Ready to Apply? Here's what you'll need to complete the application process for the Generative AI and Large Language Models Online Graduate Certificate.

Complete the Online Application

Submit your application via the online portal.

Submit Your Resume/CV

We’d like to learn more about your employment history, academic background, technical skills, and professional achievements. Submit your resume or CV showcasing your experience.

Submit Your Transcripts

Submit an unofficial copy of your transcript for each school you attended. Transcripts must include your name, the name of the college or university, the degree awarded (along with the conferral date), as well as the grade earned for each course. Email your transcripts directly to apply@online.cmu.edu. Former Carnegie Mellon students and alumni can request a copy of their CMU transcript from The Hub.

Upload a Statement of Purpose

Tell us about your academic interests, research pursuits, and long term goals. What inspires you, and what do you hope to achieve? In 500 words or less, tell us why you are interested in this certificate program and how you anticipate using it in your professional capacity.

Ready? Start Your Application Here

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