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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 $2,149

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. 

  • 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

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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