The advent of big data, cloud computing, and machine learning are revolutionizing how many professionals approach their work. These technologies offer exciting new ways for engineers to tackle real-world challenges. But with little exposure to these new computational methods, engineers lacking data science or experience in modern computational methods might feel left behind.
This two-course online certificate program brings a hands-on approach to understanding the computational tools used in modern engineering problem-solving.
Leveraging the rich experience of the faculty at the MIT Center for Computational Science and Engineering (CCSE), this program connects your science and engineering skills to the principles of machine learning and data science. With an emphasis on the application of these methods, you will put these new skills into practice in real time.
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Course 1 of 2 in the Machine Learning, Modeling, and Simulation online program
Course 2 of 2 in the Machine Learning, Modeling, and Simulation online program
We bring together an innovative pedagogy paired with world-class faculty.
Practice processes and methods through simulations, assessments, case studies, and tools.
Connect with an international community of professionals interested in solving complex problems.
Access all of the content online and watch videos on the go.
Bring your new skills to your organization, through examples from technical work environments and ample prompts for reflection.
Earn a Professional Certificate and 5 Continuing Education Units (CEUs) from MIT.
Access cutting edge, research-based multimedia content developed by MIT professors & industry experts.
You have a bachelor's degree in engineering (e.g., mechanical, civil, aerospace, chemical, materials, nuclear, biological, electrical, etc.) or the physical sciences.
You have proficient knowledge of college-level mathematics including differential calculus, linear algebra, and statistics.
You have some experience with MATLAB (R). Programming experience is not necessary, but knowledge of MATLAB (R) is very useful.
Your industry is or will be impacted by machine learning.
Prepare for this program with free online resources.
Many companies offer professional development benefits to their employees but sometimes starting the conversation is the hardest part of the process.
Use these talking points, stats, and email template to advocate for your professional development through MIT xPRO's online professional certificate program Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI.
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.
"The course was a fantastic blend of concepts and practical applications. Professor Youssef's content is unmatched to other similar courses that I've tried, and not to mention his enthusiasm for the topic is contagious."
"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."
"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."
"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."
"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."
Faculty Co-Director of MIT Center of Computational Engineering, 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
Machine Learning offers important new capabilities for solving today’s complex problems, but it’s not a panacea. To get beyond the hype, engineers and scientists must discern how and where machine learning tools are the best option — and where they are not.
Submit your information in the form above and watch a short demo video on the online program — what makes it different from other machine learning courses, what you'll learn, and how you will learn it.
