Demystify machine learning through computational engineering principles and applications. This Machine Learning program brings a hands-on approach to understanding machine learning applications in various engineering, business, and science domains
Earn a professional certificate and 10 Continuing Education Units (CEUs) from MIT xPRO
Gain insights through masterclasses from distinguished MIT faculty
Interact with a global peer group while working on real-world projects
Learn through simulations, assessments, case studies, and tools
Understand applications of machine learning in various industries and domains with this Machine Learning Engineering. Designed and delivered by MITxPro, this program brings a hands-on approach to understanding the computational tools used in engineering and business problem-solving.
This Machine Learning for Business, Engineering, and Science Program leverage MIT's thought leadership in engineering, science, and management.
Through a combination of simulations, assessments, case studies, and tools learn about applications of machine learning in various business, engineering, and physical sciences domains, and apply your knowledge to various aspects of your work.
Get started with this Machine Learning Engineering and explore everything about it.
This Machine Learning Engineering Course will help you understand the computational tools used in engineering and business problem-solving. This course covers the foundations - from modeling and simulation fundamentals to topics such as probability, and optimization to deeper concepts used in machine learning. This Machine Learning Program also takes you through the various tools/algorithms/methods used to analyze various use cases and help model the solution.
Learn how some of the computational tools used in engineering & business problem-solving are put into practice in this module. The course will take you through the practical applications of machine learning across various industries and domains through examples, case studies, and simulations. *Topics in this module could be covered through asynchronous mode/masterclasses and are subject to change at the discretion of MIT xPRO.
Attend live online interactive masterclasses conducted by distinguished MIT faculty and get insights into advancements in the AI & ML domain.
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Laurent Demanet is a Professor of Applied Mathematics, in the Department of Mathematics at MIT. He holds a joint appointment with the Department of Earth, Atmospheric, and Planetary Sciences, where he is the Director of MIT's Earth Resources Laboratory.
George Barbastathis is a professor of Mechanical Engineering at MIT. He holds a Ph.D. from Caltech. Professor Barbastathis served as a visiting scholar for Harvard University and as a research scientist for Singapore-MIT Alliance for Research and Technology Centre.
Justin Solomon is an Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT. He received a Ph.D. in computer science from Stanford University Before his graduate studies and was a member of Pixar's Tools Research group.
Richard Braatz is an Edwin R. Gilliland Professor of Chemical Engineering, MIT. Professor Braatz holds a Ph.D. and M.S from Caltech. His key interests include applied mathematics and control theory, data analytics and machine learning, and materials processing.
McAfee Professor of Engineering & Head of the Department of Civil & Environmental Engineering at MIT. Professor Buehler holds a Postdoctoral Scholar, the Division of Chemistry and Chemical Engineering, Caltech, and a P.h.D in Materials Science from the University of Stuttgart.
Themistoklis Sapsis is an Associate Professor of Mechanical & Ocean Engineering at MIT. His interests include the quantification and prediction of extremes events in complex systems and targeted energy transfers in mechanical systems
Heather Kulik is an Associate Professor of Chemical Engineering, at MIT. Professor’s research interests focus on catalysis, transition-metal chemistry, electronic structure methods, atomistic simulations, and enzyme catalysis. She has achieved many honors and awards.
This program caters to working professionals from a variety of industries and backgrounds; the diversity of our students adds richness to class discussions and interactions.
The application process is organized and led by Simplilearn and consists of 3 simple steps. An admission offer will be made to selected candidates and accepted by paying the fee.
Complete the application process by submitting the required documents
An admissions panel will shortlist candidates based on their application
Selected candidates can access the program from the batch start date
Program is best suited for:
The admission fee for this program is $ 2,375
We are dedicated to making our programs accessible. We are committed to helping you find a way to budget for this program and offer a variety of financing options to make it more economical.
You can pay monthly installments for Post Graduate Programs using Splitit, Affirm or ClimbCredit payment option with low APR and no hidden fees.
We provide the following options for one-time payment
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Data Sharing Policy
MIT xPRO is collaborating with online education provider Simplilearn to deliver this Machine Learning Engineering Course through a dynamic, interactive, digital learning platform. This course leverages MIT xPRO's thought leadership in engineering and management practice developed over years of research, teaching, and practice. Accessibility
CONSENT TO RELEASE LEARNER INFORMATION
I hereby authorize the Massachusetts Institute of Technology ("MIT") to release
identified information relating to me to Simplilearn, as described below:
Information to be released:
• Learner data including course progress and completion
• Demographic information from the learner profile including gender, year of birth, and level of education
• Learner data collected through survey instruments and the online discussions boards
This program is best suited for:
Professionals looking to understand the applications of machine learning across various engineering, science, and business fields
Professionals with a bachelor's degree in engineering (e.g., mechanical, civil, aerospace, chemical, materials, nuclear, biological, electrical, etc.), business, or physical sciences
Professionals with a background in college-level mathematics including differential calculus, linear algebra, and statistics
Programming experience is not necessary, but some experience with MATLAB (R) is very useful
To ensure money is not a limiting factor in learning, we offer various financing options to help make this program financially manageable. For more details, please refer to our "Admissions Fee and Financing" section
As a part of this machine learning engineering program, you will receive the following:
Professional certificate of completion from MIT xPRO
10 Continuing Education Units (CEUs) from MIT
Live online masterclasses from distinguished MIT faculty
Connect with an international community of professionals
Work on real-world projects
MIT xPRO is collaborating with online education provider Simplilearn to deliver this online program. This course leverages MIT xPRO's thought leadership in machine learning engineering, science, and management developed over years of research, teaching, and practice. Upon completion of this program, you will be awarded a certificate from MIT xPRO.