Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI Program is a 10-week program comprising two 5-week courses that will teach you the foundational conceptual underpinnings of machine learning, including how machine learning algorithms are created and how ML systems function, and explore different applications of machine learning processes across science and industry.
This program teaches the foundations of machine learning, from the ground up, as a tool for modeling and prediction that builds on other, traditional tools from modeling, simulation, and prediction. You will learn how complex physical processes can be simulated by discretizing differential equations, and come to understand optimization techniques as a foundation for machine learning.
The program is well-suited for scientists, researchers, engineers, and other technical professionals looking to expand their ability to model and simulate systems to produce and refine data and make better decisions.
The technical nature of the program requires that learners be proficient in several areas of undergraduate-level mathematics, and makes heavy use of MATLAB to present a guided environment to explore the foundations of machine learning within. Prior experience with MATLAB and computer programming will be helpful, but is not necessary! Please refer to this FAQ article for more information.
The first course, Machine Learning, Modeling, and Simulation Principles, focuses on building the foundations and principles of machine learning by reviewing modeling, simulation, and optimization methods. The coursework features practice activities with immediate feedback as well as graded assignments that use guided MATLAB code, permitting learners to experiment with and develop an intuition for the methods, their strengths, and weaknesses. Finally, learners are presented with case studies from Schlumberger, Aurora Flight Sciences, and BASF—companies applying machine learning methods to real-world, practical problems—in order to apply their knowledge. Please review the Course Schedule for more details about the topics covered in Course 1!
The second course, Applying Machine Learning to Engineering and Science, focuses more directly on the applications of machine learning to engineering and science, acting as a “tour” cutting across many different areas of science and industry. It features presentations from MIT faculty, across diverse fields—including chemical, mechanical, and aerospace engineering, and geo and materials sciences—about how they apply machine learning methods to important problems in their fields. The course’s assignments are presented as case studies, with further material made available as optional readings. Please review the Course Overview for more details about the topics covered in Course 2!
Because this program's first course, Machine Learning, Modeling, and Simulation Principles, primarily covers the foundations and principles of machine learning, and the second course, Applying Machine Learning to Engineering and Science, primarily explores applications of machine learning, the Course Team strongly recommends that most learners take the Machine Learning program's courses in sequential order. Prospective learners who hold a master’s or higher degree in Engineering, are comfortable with the math pre-requisite, Linear Algebra, and have work experience in this or a related field within the last 5 years are likely to find that they are able to take course 2 first, but others should take course 1 first.