Applying Machine Learning to Engineering and Science is a 5-week course that will explore the applications of Machine Learning across a range of diverse fields in engineering and the sciences.
This course continues where Course 1, Machine Learning, Modeling, and Simulation Principles, ends, moving to the applications of machine learning. While machine learning methods apply to problems in many fields, like predicting consumer behavior, analyzing human preferences, remodeling huge repositories of text, speech, or images, this course specifically focuses on how machine learning applies to engineering and science applications, which often look very different—they often demand high levels of accuracy and reliability, large data sets are often unavailable, and prior physical knowledge and models may need to be fused with newer, data-driven insights.
The course is well-suited for scientists, researchers, engineers, and others looking to expand their ability to model and simulate systems to produce and refine data and make better decisions.
The technical nature of the course 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 course features presentations from 9 MIT faculty, across diverse fields—including chemical, mechanical, and aerospace engineering, and geo- and materials sciences—detailing 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.
Explore the applications of machine learning to problems of battery life prediction, computational imaging, geophysics and seismology, predicting oil and gas production, computer-aided design and computational geometry, quantifying risk in complex systems from offshore wind platforms to a variety of civil infrastructure, materials discovery in chemistry and catalysis, materials design for structural materials, and biomaterials, as well as machine learning approaches to inverse problems and data assimilation for problems from X-ray imaging and tomography to weather prediction.
The course will complete your journey through the foundational principles and applications of machine learning, connecting the underlying numerical methods of modeling simulation and prediction to current applications of machine learning in engineering and the sciences.
Because this second course, Applying Machine Learning to Engineering and Science, primarily explores applications of machine learning, while the program's first course, Machine Learning, Modeling, and Simulation Principles, primarily covers the foundations and principles 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.