SPOC
Applying Machine Learning to Engineering and Science
Master key machine learning techniques to solve engineering and science problems with case studies and real-world applications
Learning Platform
MIT xPRO Learning Platform
Location
Online
Duration
5 Weeks
Upcoming Sessions
Nov. 03 – Dec. 07, 2025
Outcomes
Use of Active Learning for optimal experimental design
Exploring Dimensionality Reduction techniques in ML
Optimization of Surrogate Models in simulations
Applying Bayesian Approaches to aerospace challenges
Implementing Risk Quantification in complex systems
You Will Get
Official Certificate From MIT
Elevate your credentials with globally recognized certifications and 2.5 CEUs.
Live Interactive Sessions
Select private cohort SPOCs have the opportunity to meet and interact with the professors who guide the SPOC video content in special one-hour seminars.
Modules
MODULE
1
Feature Engineering in Li-Ion Battery Life Prediction
Learners will apply feature engineering techniques to predict battery life.
Learn about feature identification and regularization techniques.
Understand the role of feature engineering in predicting battery lifespan.
Complete a graded assignment on feature engineering application.
MODULE
2
Machine Learning for Computational Imaging
Learners will utilize machine learning to solve imaging problems.
Explore inverse problems and phase retrieval techniques.
Learn neural networks for image synthesis and tomography.
Engage in a graded assignment on computational imaging.
MODULE
3
Seismic Deepfakes – Neural Nets to Generate Missing Data
Learners will use neural networks to handle missing data in seismic applications.
Study seismic waves and wave equations.
Implement neural networks for seismic data recovery.
Complete a graded assignment on seismic deepfakes.
MODULE
4
Prediction of Oil and Gas Production
Learners will predict future oil and gas production using machine learning.
Learn linear regression techniques for energy production data.
Analyze and predict future production rates with machine learning models.
Participate in a graded assignment on oil production prediction.
MODULE
5
Machine Learning in Geometric Representations
Learners will apply machine learning to geometric data and 3D point clouds.
Understand geometric data processing using machine learning.
Work on point cloud and vector data analysis.
Engage in a graded assignment focusing on 3D geometric learning.
MODULE
6
Quantifying Risk in Complex Systems Using Machine Learning
Learners will use machine learning to assess risk in complex systems.
Study probabilistic approaches to extreme events.
Learn about active learning and experimental design for risk quantification.
Complete a graded assignment on risk modeling.
MODULE
7
Machine Learning for Accelerating Computational Materials Discovery
Learners will accelerate materials discovery with machine learning.
Analyze inorganic chemistry data with machine learning models.
Explore feature selection and uncertainty quantification techniques.
Engage in a graded assignment on materials discovery.
MODULE
8
Practical Machine Learning in Composite Design
Learners will use machine learning for designing advanced materials.
Learn materials science fundamentals and apply machine learning to materials design.
Use ML for image classification and fracture propagation prediction.
Complete a graded assignment on composite material design.
MODULE
9
Machine Learning in Aerospace
Learners will apply machine learning to solve aerospace challenges.
Explore inverse problems and Bayesian approaches in aerospace.
Learn dimensionality reduction and surrogate modeling techniques.
Participate in a graded assignment on aerospace applications.
Academic Team
Youssef M. Marzouk
Director of the MIT Center of Computational Engineering, Associate Professor of Aeronautics, and Director of the Aerospace Computational Design Laboratory at MIT
He is also a core member of MIT's Statistics and Data Science Center.
His research interests include computational mathematics, uncertainty.
Heather Kulik
Associate Professor of Chemical Engineering at MIT
Her research interests include computational chemistry, transition metal chemistry, molecular design, density function theory, and enzyme catalysis
Richard Braatz
Professor of Chemical Engineering at MIT
His research interests include systems theory, systems and control theory, fault diagnosis, manufacturing processes, and manufacturing systems
From Our Students
"The course allowed me to put new knowledge into practice immediately through assignments, which deepened my understanding. The professors offered valuable insights and broadened my perspective, making the learning journey truly enriching."
Anderson W.
Peking University
College of Engineering
FAQs
Can I use the assistance of generative AI in my course?
No, unlike our policies for PBLs, using LLMs like ChatGPT, Google Bard, or Claude AI for SPOC assignments is strictly prohibited. Violations may result in severe consequences, including a failing grade, losing your certificate, or being banned from future courses.
Will I receive MIT credits for completing the course?
No, MIT xPRO courses are non-credit professional development programs. However, you can earn Continuing Education Units (CEUs) recognized in the US as evidence of your professional development.
What are the course and assignment deadlines?
Your SPOC platform will have a timeline that shows when assignments are due. Many SPOCs require all assignments to be submitted by the course end date, however this is not true across the board. Deadlines are strict, and late submissions are not accepted, however some courses allow dropping your lowest scores to accommodate missed assignments. Check your specific course policy for details.
Will I have access to the course material after the course ends?
Yes, you’ll have indefinite access to archived course materials after the program concludes. However, live support (instructors and forums) is available for only 30 days post-course. Downloadable materials remain accessible forever.
Will I receive an official certificate?
Yes, you will receive an official certificate of completion for a SPOC, provided you meet the following criteria:
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Complete All Course Modules: Finish all required modules, including readings and activities.
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Pass Required Assessments: Achieve passing scores on quizzes, assignments, or exams set for the course.
Meeting these standards confirms your understanding and engagement, qualifying you for the certificate.
In what language are courses taught?
All courses are conducted in English.
Are courses live or pre-recorded?
Courses are pre-recorded for flexibility, allowing you to access materials anytime.
Can I interact with instructors or other learners?
Yes, there are discussion forums on the MIT xPRO platform. Some SPOCs may also feature optional live sessions with the instructor.
What time zone are deadlines listed in?
Deadlines are in UTC. Use this time converter to match it with your local time.
What are the technical requirements for the course?
For the best experience, using the latest versions of Chrome or Firefox is recommended. Mobile access is limited, so completing the course on a laptop or desktop is recommended.
Are video captions available?
Yes, all videos have captions and downloadable transcripts for easier study and review.
How do I complete a course?
To complete a course, fulfill the requirements for scored assignments. You can find detailed information on assignments and deadlines in the "Get Started" tab on the MIT xPRO Learning Platform after enrolling.
Are grades awarded?
MIT xPRO uses a pass/fail system; no specific grades are given. You must earn a minimum number of points to pass and receive a certificate. Be sure to review the Course Schedule for deadlines, as late submissions are not accepted.