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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  • START DATE November 2, 2020 More Dates
  • TIME COMMITMENT 4-6 hours per week
  • DURATION 5 weeks
  • FORMAT Online
  • PRICE $1,350

WHAT YOU WILL LEARN

Learn how the computational tools used in engineering problem-solving are put into practice in course 2 of this 2-course program. View the weekly schedule here.

  • 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

  • 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, MIT

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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