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.

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  • START DATE January 24, 2022 More Dates
  • TIME COMMITMENT 4-6 hours per week
  • DURATION 5 weeks per course
  • FORMAT Online
  • PRICE $2,149

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 course is also offered in Spanish. Enrollment is managed through our collaborator, 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.

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HOW YOU WILL LEARN

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

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

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

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

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

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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MIT FACULTY AND INSTRUCTORS

Youssef M. Marzouk

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

George Barbastathis

Professor of Mechanical Engineering

Heather Kulik

Heather Kulik

Associate Professor of Chemical Engineering, MIT

John Williams

John Williams

Professor Civil & Environmental Engineering, MIT

Themistoklis Sapsis

Themistoklis Sapsis

Associate Professor of Mechanical & Ocean Engineering, MIT

Markus Buehler

Markus Buehler

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

Richard Braatz

Richard Braatz

Edwin R. Gilliland Professor of Chemical Engineering, MIT

Justin Solomon

Justin Solomon

Associate Professor of Electrical Engineering and Computer Science, MIT

Laurent Demanet

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 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 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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