Machine Learning Applied to Engineering and Science offered by MIT University
- Private University
168 acre campus- Estd. 1861
Machine Learning Applied to Engineering and Science at MIT University Overview
Machine Learning Applied to Engineering and Science
at MIT University
Delve into the practical implementation of machine learning algorithms in engineering contexts
Duration | 5 weeks |
Total fee | ₹1.22 Lakh |
Mode of learning | Online |
Official Website | Go to Website  |
Course Level | UG Certificate |
Machine Learning Applied to Engineering and Science at MIT University Highlights
Machine Learning Applied to Engineering and Science
at MIT University
- Earn a professional certificate and continuing professional education credits from MIT
- Learn through simulations, assessments, case studies, and tools
- Get a chance to connect with an international community of professionals
Machine Learning Applied to Engineering and Science at MIT University Course details
Machine Learning Applied to Engineering and Science
at MIT University
Skills you will learn
Who should do this course?
- For Industry professionals
- For Other technical professionals
What are the course deliverables?
- Understand why and how machine learning methods may improve engineering problem-solving
- Learn how researchers make better predictions with missing or sparse data
- Quantify risk and clarify salient features from data in complex systems
- Transfer machine learning approaches developed in one industry to another industry
- Assess conditions when a machine learning approach may not be helpful or worth the extra effort
More about this course
- This course equips participants with the knowledge and skills to leverage ML techniques specifically tailored for solving complex problems within engineering and scientific domains, enabling them to contribute to innovative advancements in their respective fields
- This course integrates machine learning methodologies with practical applications in engineering and scientific domains
Machine Learning Applied to Engineering and Science at MIT University Curriculum
Machine Learning Applied to Engineering and Science
at MIT University
Predictive Modeling
Pattern Recognition and Classification
Data Mining and Exploration
Optimization and Control
Anomaly Detection and Quality Control
Simulation and Modeling
Feature Engineering and Selection
Uncertainty Quantification
Time-Series Analysis and Forecasting
Machine Learning Applied to Engineering and Science at MIT University Faculty details
Machine Learning Applied to Engineering and Science
at MIT University
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
Machine Learning Applied to Engineering and Science at MIT University Entry Requirements
Machine Learning Applied to Engineering and Science
at MIT University
Eligibility criteria
- Education: Graduate
Conditional Offer
- No
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Machine Learning Applied to Engineering and Science at MIT University Contact Information
Machine Learning Applied to Engineering and Science
at MIT University
Address
77 Massachusetts Ave, Cambridge, MA 02139, USA
Cambridge ( Massachusetts)
Phone
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