Machine Learning Applied to Engineering and Science
offered by MIT USA

Private University
168 acre campus
Estd. 1861

Machine Learning Applied to Engineering and Science
at
MIT USA
Overview

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

Table of content

Overview
Highlights
Course Details
Curriculum
Faculty
Entry Requirements

Machine Learning Applied to Engineering and Science
at
MIT USA
Highlights

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 USA
Course details

Skills you will learn

Data mining Quality control Simulation Predictive modeling Forecasting

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 USA
Curriculum

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 USA
Faculty details

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