- START DATE April 12, 2021
- TIME COMMITMENT 4-6 hours per week
- DURATION 5 weeks
- FORMAT Online
- PRICE $1,350
WHAT YOU WILL LEARN
- Simulate physical processes using numerical discretization methods.
- Assess cost-accuracy trade-offs in numerical simulation.
- Learn powerful optimization techniques and understand their fundamental role in machine learning.
- Describe canonical machine learning problems from a statistical perspective.
- Practice real-world forecasting and risk assessment problems using Monte Carlo simulation.
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
MIT Center for Computational Science and Engineering
MIT CCSE Faculty
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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