Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI
Demystify machine learning through computational engineering principles and applications in this two-course program from MIT.
- TIME COMMITMENT 4-6 hours per week
- DURATION 5 weeks per course
- FORMAT Online
- PRICE $2,319
WHAT YOU WILL LEARN
This two-course online certificate program brings a hands-on approach to understanding the computational tools used in engineering problem-solving.
This program is also delivered in SPANISH (Machine Learning, Modelos y Simulación: Ingeniería en la Era de IA) in collaboration with Global Alumni.
- Learn how to simulate complex physical processes in your work using discretization methods and numerical algorithms.
- Assess and respond to cost-accuracy tradeoffs in simulation and optimization, and make decisions about how to deploy computational resources.
- Understand optimization techniques and their fundamental role in machine learning.
- Practice real-world forecasting and risk assessment using probabilistic methods.
- Recognize the limitations of machine learning and what MIT researchers are doing to resolve them.
- Learn about current research in machine learning at the MIT CCSE and how it might impact your work in the future.
WHO SHOULD ENROLL
Industry professionals with at least a bachelor's degree in engineering (e.g., mechanical, civil, aerospace, chemical, materials, nuclear, biological, electrical, etc.) or the physical sciences.
Other technical professionals with a background in college-level mathematics including differential calculus, linear algebra, and statistics.
Programming experience not necessary, but some experience with MATLAB (R) is very useful.
HOW YOU WILL LEARN
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LEARN BY DOING
Practice processes and methods through simulations, assessments, case studies, and tools.
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LEARN FROM OTHERS
Connect with an international community of professionals while working on projects based on real-world examples.
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LEARN ON DEMAND
Access all of the content online and watch videos on the go.
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REFLECT AND APPLY
Bring your new skills to your organization, through examples from technical work environments and ample prompts for reflection.
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DEMONSTRATE YOUR SUCCESS
Earn a Professional Certificate and 5 Continuing Education Units (CEUs) from MIT.
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LEARN FROM THE BEST
Gain insights from leading MIT faculty and industry experts.
WHAT LEARNERS ARE SAYING
MIT XPRO LEARNERS ARE NOT ONLY SCIENTISTS, ENGINEERS, TECHNICIANS, MANAGERS AND CONSULTANTS – THEY ARE CHANGE AGENTS. THEY TAKE THE INITIATIVE, PUSH BOUNDARIES, AND DEFINE THE FUTURE.
Vivian D'Souza, Model Based Systems Analysis Engineer at Dana Incorporated
The course was a fantastic blend of concepts and practical applications. Professor Youssef's content is unmatched to other similar courses that I've …
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Rachael Naoum, Product Definition Engineer at Dassault Systems
I loved this course. At first, I was a bit intimidated, it's been a while since I've done any hardcore math. However, the layout of this course made …
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Tolga Kaya, Professor of Electrical and Computer Engineering at Sacred Heart University
This course allowed me to dig deeper [into] the foundations of machine learning and the underlying mechanism of the main algorithms that are used. As…
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Bill Bear, Agile Transformation Coach
Great course for learning the concepts and methods behind machine learning! The course was prepared and delivered in a thoughtful way that provided …
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Juharasha Shaik, Senior Staff Software Engineer at Visa
This course has helped me gain more understanding of the various algorithms that can be applied to the problems that we face during data analysis and…
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Rachael Naoum, Product Definition Engineer at Dassault Systems
I loved this course. At first, I was a bit intimidated, it's been a while since I've done any hardcore math. However, the layout of this course made it super easy to follow along with all the concepts and I felt very well-guided throughout the graded assignments. I like how the lectures are broken into short videos for each topic, making it easy to replay and digest. Very well-thought-out course.
Tolga Kaya, Professor of Electrical and Computer Engineering at Sacred Heart University
This course allowed me to dig deeper [into] the foundations of machine learning and the underlying mechanism of the main algorithms that are used. As a MATLAB user, I particularly appreciated the utilization of MATLAB instead of straight black box python libraries.
Bill Bear, Agile Transformation Coach
Great course for learning the concepts and methods behind machine learning! The course was prepared and delivered in a thoughtful way that provided good challenges and plenty of helpful information. This is just the kind of result that I have come to expect with MIT xPRO.
Juharasha Shaik, Senior Staff Software Engineer at Visa
This course has helped me gain more understanding of the various algorithms that can be applied to the problems that we face during data analysis and modeling. I definitely recommend this course [for a] great understanding of the available tools/algorithms/methods to analyze various use cases and help model the solution.
MIT FACULTY AND 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
Heather Kulik
Associate Professor of Chemical Engineering, 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
COURSES IN THIS PROGRAM
To earn a Professional Certificate, you must complete both courses in the program. For those who do not want to commit to the full program, courses can be taken on an individual basis. Savings apply when enrolling into the full program.
Machine Learning, Modeling, and Simulation Principles
Course 1 of 2 in the program Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI
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Applying Machine Learning to Engineering and Science
Course 2 of 2 in the program Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI
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