Machine Learning for new age AI

What you will learn

Understand and Learn how the computational tools used in engineering problem-solving are put into practice in this 2-course program.

Simulate physical processes using numerical discretization methods.

Assess cost-accuracy trade-offs in numerical simulation.

Learn powerful optimization techniques and understand their fundamental role in machine learning.

Describe canonical machine learning problems from a statistical perspective.

Practice real-world forecasting and risk assessment problems using Monte Carlo simulation.

Understand why and how machine learning methods may improve engineering problem-solving.

Learn how researchers make better predictions with missing or sparse data.

Transfer machine learning approaches developed in one industry to another industry.

Quantify risk and clarify salient features from data in complex systems.

Assess conditions when a machine learning approach may not be helpful or worth the extra effort.

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How you will learn

LEARN BY DOING

Practice processes and methods through simulations, assessments, case studies, and tools.
LEARN FROM OTHERS

Connect with an international community of professionals while working on projects based on real-world examples.
LEARN ON DEMAND

Access all of the content online and watch videos on the go.

REFLECT AND APPLY

Bring your new skills to your organization, through examples from technical work environments and ample prompts for reflection.
DEMONSTRATE YOUR SUCCESS

Earn a Professional Certificate and 2.5 Continuing Education Units (CEUs) from MIT.
LEARN FROM THE BEST

Gain insights from leading MIT faculty and industry experts.

MIT Faculty & Industry Experts

Meet your instructors

Youssef M. Marzouk

Faculty Co-Director of MIT Center of Computational Engineering, Associate Professor of Aeronautics & Astronautics and Director of Aerospace Computational Design Laboratory, MIT.

George Barbastathis

Professor of Mechanical Engineering, MIT.

Heather Kulik

Associate Professor of Chemical Engineering, MIT

Aram Harrow

Associate Professor of Physics at MIT

John Williams

Professor Civil & Environmental Engineering, MIT.

Themistoklis Sapsis

Associate Professor of Mechanical & Ocean Engineering, MIT.

Markus Buehler

McAfee Professor of Engineering & Head, Department of Civil & Environmental Engineering, MIT.

Richard Braatz

Edwin R. Gilliland Professor of Chemical Engineering, MIT.

Justin Solomon

Associate Professor of Electrical Engineering and Computer Science, MIT.

Laurent Demanet

Professor of Applied Mathematics & Director of MIT's Earth Resources Laboratory.

MIT Center for Computational Science and Engineering

MIT CCSE Faculty

Courses In this Program

To earn a Professional Certificate and CEUs, you must complete the two courses in the program.

Machine Learning, AI-based Modeling, and Simulation Principles

Course 1 of 2 in the program is Machine Learning, AI-based Modeling, and Simulations which involve engineering problem-solving in the age of AI.
Applying Machine Learning to Business, Engineering, and Science

Course 2 of 2 in the program is Applying Machine Learning to Business, Engineering, and Science which includes use cases of machine learning in various industries.

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Frequently Asked Questions

What will I gain with this program?
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- This program will help you acquire deeper foundational knowledge of machine learning from technical and business perspectives.
- Learn from the best in the world, the distinguished MIT faculty, and earn your certificate from MIT.
- Connect with an international community of professionals
- Work on real-world projects
Is this just one program?
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Yes, however, there are two courses under this program:
- Course 1: Machine Learning, AI-based Modeling, and Simulations This course has a duration of 5 weeks and only 4-6 hours per day of learning is required to complete the course.
- Course 2: Applying Machine Learning to Business, Engineering, and Science This course has a duration of another 5 weeks and only 4-6 hours per day of learning is required to complete this course.
How to apply for the program?
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You can enroll for this program through USAII^®, since they are the marketing and delivery partner of MIT xPRO for this program.
- Click on the Enroll Now button
- Fill in your details to apply for the program
- Once applied, your candidature will be reviewed for approval
- Meanwhile, you’ll receive access to the USAII^® dashboard
- After approval, you’ll receive an email from MIT on access to your MIT dashboard and the program orientation.
What is the refund policy?
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For MIT xPRO Programs:
- On behalf of MIT xPRO, USAII^® will enroll you in the program and collect the program fees. If you cancel the program seven (7) days before the start of the program, USAII^® will refund the program fee after deducting the processing fee of $249.
- Any requests for withdrawal within seven (7) days of the start of the program will not be refunded.
For Dual Certification Programs:
- If you applied for the dual certification program from MIT xPRO and USAII^® and wish to cancel the dual program within 24 hours of payment, USAII^® will refund the program fee after deducting the processing fee of $249.
- If you wish to cancel only the USAII^® program within 24 hours of payment, USAII^® will refund the ‘USAII^® program fee’ after deducting the processing fee of $147.. For withdrawal requests beyond 24 hours, the fee paid will not be refunded.
- For any cancellation requests for the MIT xPRO program, please refer to the above-mentioned refund policy ‘For MIT xPRO Programs’.
Can I apply for Speedster discounts?
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Speedster discounts are only for the USAII^® certification programs and not for MIT xPRO programs. However, if USAII^® is running any offers/campaigns you can receive the voucher codes that can be utilized while registering for the MIT xPRO programs.
Can I pay in parts or installments?
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Yes, you can pay in two-part installments.
- The total program fee is divided into two (2) equal parts.
- This is an interest-free payment option.
- The first payment must be paid during your registration, which is 50% of the program fee.
- The second or final installment must be paid 7 days before the start of the program or earlier.
- In case of non-payment of the final installment, your candidature will be canceled and your first installment will be refunded.
- For refunds, please refer to the refund policy.

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What you will learn

Simulate physical processes using numerical discretization methods.

Assess cost-accuracy trade-offs in numerical simulation.

Learn powerful optimization techniques and understand their fundamental role in machine learning.

Describe canonical machine learning problems from a statistical perspective.

Practice real-world forecasting and risk assessment problems using Monte Carlo simulation.

Understand why and how machine learning methods may improve engineering problem-solving.

Learn how researchers make better predictions with missing or sparse data.

Transfer machine learning approaches developed in one industry to another industry.

Quantify risk and clarify salient features from data in complex systems.

Assess conditions when a machine learning approach may not be helpful or worth the extra effort.

Who Should Enroll

PROGRAM FEE: $2,375 / $2,255

How you will learn

LEARN BY DOING

LEARN FROM OTHERS

LEARN ON DEMAND

REFLECT AND APPLY

DEMONSTRATE YOUR SUCCESS

LEARN FROM THE BEST

MIT Faculty & Industry Experts

Youssef M. Marzouk

George Barbastathis

Heather Kulik

Aram Harrow

John Williams

Themistoklis Sapsis

Markus Buehler

Richard Braatz

Justin Solomon

Laurent Demanet

MIT Center for Computational Science and Engineering

Courses In this Program

Machine Learning, AI-based Modeling, and Simulation Principles

Applying Machine Learning to Business, Engineering, and Science

Dual Certification Programs