- START DATE November 2, 2020
- 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.
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 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
Professor of Mechanical Engineering, MIT
Associate Professor of Chemical Engineering, MIT
Professor Civil & Environmental Engineering, MIT
Associate Professor of Mechanical & Ocean Engineering, MIT
McAfee Professor of Engineering & Head, Department of Civil & Environmental Engineering, MIT
Edwin R. Gilliland Professor of Chemical Engineering, MIT
Associate Professor of Electrical Engineering and Computer Science, MIT
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.
Course 1 of 2 in the program Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AIview detail
Course 2 of 2 in the program Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AIview detail
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