Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI offered by Massachusetts Institute of Technology
- Private University
168 acre campus- Estd. 1861
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI at MIT Overview
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI
at MIT
Demystify machine learning through computational engineering principles and applications in this two-course program from MIT
Duration | 10 weeks |
Total fee | ₹1.84 Lakh |
Mode of learning | Online |
Course Level | UG Certificate |
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI
Table of content
- Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI Overview
- Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI Highlights
- Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI Course Details
- Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI Curriculum
- Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI Faculty
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI at MIT Highlights
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI
at MIT
- Earn a Professional Certificate and 5 Continuing Education Units (CEUs) from MIT
- Learn through simulations, assessments, case studies, and tools
- Get a chance to connect with an international community of professionals
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI at MIT Course details
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI
at MIT
Skills you will learn
Who should do this course?
- For Industry professionals
- For Other technical professionals
What are the course deliverables?
- Learn how to simulate complex physical processes in your work using discretization methods and numerical algorithms
- Assess and respond to cost-accuracy tradeoffs in simulation and optimization, and make decisions about how to deploy computational resources
- Understand optimization techniques and their fundamental role in machine learning
- Practice real-world forecasting and risk assessment using probabilistic methods
- Recognize the limitations of machine learning and what MIT researchers are doing to resolve them
- Learn about current research in machine learning at the MIT CCSE and how it might impact your work in the future
More about this course
- This two-course online certificate program brings a hands-on approach to understanding the computational tools used in engineering problem-solving
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI at MIT Curriculum
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI
at MIT
Machine Learning, Modeling, and Simulation Principles
Applying Machine Learning to Engineering and Science
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI at MIT Faculty details
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI
at MIT
John Williams
John Williams holds a BA in Physics from Oxford University, an MS in Physics from UCLA, and a PhD in Numerical Methods from the University of Wales, Swansea.
Heather Kulik
Markus Buehler
Richard Braatz
Other courses offered by MIT
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Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI at MIT Contact Information
Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI
at MIT
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77 Massachusetts Ave, Cambridge, MA 02139, USA
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