Machine Learning for Business, Engineering, and Science
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
- Next Cohort starts 26 Jun, 2023
- Program Duration 4 months
- Learning Format Online Bootcamp
World’s #1 Online Bootcamp
Why Join this Program
Program Overview
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.
Key Features
- Professional certificate of completion from MIT xPRO
- 8 Continuing Education Units (CEUs) from MIT xPRO
- Live online masterclasses from distinguished MIT faculty
- Connect with an international community of professionals
Program Advantage
This Machine Learning for Business, Engineering, and Science Program leverage MIT's thought leadership in engineering, science, and management.
About MIT xPRO:
- Professional certificate of completion from MIT xPRO
- 8 Continuing Education Units (CEUs) from MIT xPRO
- Live online masterclasses from distinguished MIT faculty
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Program Details
Through a combination of simulations, assessments, case studies, and tools learn about applications of machine learning in various business, engineering, and physical sciences disciplines, and apply your knowledge to various aspects of your work.
Learning Path
Get started with this Machine Learning Engineering course 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.
Electives:
Attend live online interactive masterclasses conducted by distinguished MIT faculty and get insights into advancements in the AI & ML domain.
Skills Covered
- Machine learning applications
- Optimization techniques
- Simulations
- Probability and statistics
- Realworld forecasting
- Risk assessment and quantification
Case Studies
Program Advisors
Youssef. M. Marzouk
Professor of Aeronautics and AstronauticsYoussef M. Marzouk is e Professor of Aeronautics and Astronautics, Co-Director of the MIT Center for Computational Science and Engineering, Professor of Aeronautics & Astronautics.
John R. Williams
Professor Civil & Environmental EngineeringJohn R. Williams is a Professor of Information Engineering and Civil and Environmental Engineering at MIT. Professor Williams holds a BA in physics from Oxford University, an M.Sc. in physics, and a Ph.D.
Heather Kulik
Associate Professor of Chemical EngineeringHeather 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.
George Barbastathis
Professor of Mechanical EngineeringGeorge 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.
Themistoklis Sapsis
Professor of Mechanical EngineeringThemistoklis Sapsis is a Professor of Mechanical Engineering at MIT. His interests include the quantification and prediction of extremes events in complex systems and targeted energy transfers in mechanical systems
Markus J. Buehler
McAfee Professor of EngineeringMarkus J. Buehler is the McAfee Professor of Engineering at MIT, a member of the Center for Materials Science and Engineering, and the Center for Computational Science and Engineering at the Schwarzman College of Computing.
Richard D. Braatz
Edwin R. Gilliland Professor of Chemical EngineeringRichard 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.
Justin Solomon
Associate Professor of Electrical Engineering and Computer ScienceJustin Solomon is an Associate 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.
Laurent Demanet
Professor of MathematicsLaurent Demanet is a Professor 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.
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/MIT_machine_learning/Heather.jpg "Heather Kulik")
/MIT_machine_learning/George.jpg "George Barbastathis")
/MIT_machine_learning/Themistoklisis.jpg "Themistoklis Sapsis")
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/MIT_machine_learning/Justin.jpg "Justin Solomon")
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Batch Profile
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 class consists of learners from excellent organizations and diverse industriesIndustryAerospace - 12%Automotive - 6%Energy - 9%IT/ITES - 37%Manufacturing - 15%Others - 21%Companies
Learner Reviews
Admission Details
Eligibility Criteria
Program is best suited for:
Admission Fee & Financing
The admission fee for this program is $ 2,375
Financing Options
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.
Pay in Installments
You can pay monthly installments for Post Graduate Programs using Splitit, Affirm, ClimbCredit or Klarna payment option with low APR and no hidden fees.
Other Payment Options
We provide the following options for one-time payment
- Credit Card
- Paypal
$ 2,375
Program Cohorts
Next Cohort
FAQs
Data Sharing Policy
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
Who is this Machine Learning program best suited for?
This program is best suited for:
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Professionals looking to understand the applications of machine learning across various engineering, science, and business fields
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Professionals with a bachelor's degree in engineering (e.g., mechanical, civil, aerospace, chemical, materials, nuclear, biological, electrical, etc.), business, or physical sciences
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Professionals with a background in college-level mathematics including differential calculus, linear algebra, and statistics
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Programming experience is not necessary, but some experience with MATLAB (R) is very useful
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Will financial aid be provided for this Machine Learning for Business, Engineering, and Science program?
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
What should I expect from this Machine Learning for Business, Engineering and Science program?
As a part of this machine learning engineering program, you will receive the following:
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Professional certificate of completion from MIT xPRO
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8 Continuing Education Units (CEUs) from MIT xPRO
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Live online masterclasses from distinguished MIT faculty
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Connect with an international community of professionals
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Work on real-world projects
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What certificate will I receive?
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.
- Disclaimer
- PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.