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
- TIME COMMITMENT 4-6 hours per week
- DURATION 5 weeks
- FORMAT Online
WHAT YOU WILL LEARN
Understand the computational tools used in engineering problem-solving in course 1 of this 2-course program.
View the week by week schedule here.
This course is also offered in SPANISH (Machine Learning: Modelos y Principios de Simulación) in collaboration with Global Alumni.
- 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
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LEARN BY DOING
Practice processes and methods through simulations, assessments, case studies, and tools.
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LEARN FROM OTHERS
Connect with an international community of professionals while working on projects based on real-world examples.
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LEARN ON DEMAND
Access all of the content online and watch videos on the go.
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REFLECT AND APPLY
Bring your new skills to your organization, through examples from technical work environments and ample prompts for reflection.
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DEMONSTRATE YOUR SUCCESS
Earn a Professional Certificate and 2.5 Continuing Education Units (CEUs) from MIT.
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LEARN FROM THE BEST
Gain insights from leading MIT faculty and industry experts.
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, 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, 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, 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, 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, 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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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 it super easy to follow along with all the concepts and I felt very well-guided throughout the graded assignments. I like how the lectures are broken into short videos for each topic, making it easy to replay and digest. Very well-thought-out course.
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 a MATLAB user, I particularly appreciated the utilization of MATLAB instead of straight black box python libraries.
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 good challenges and plenty of helpful information. This is just the kind of result that I have come to expect with MIT xPRO.
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 modeling. I definitely recommend this course [for a] great understanding of the available tools/algorithms/methods to analyze various use cases and help model the solution.
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.
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
view detail
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
view detailTHE BEST COMPANIES CONNECT WITH THE BEST MINDS AT MIT
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