Applying Machine Learning to Engineering and Science is a 5-week course designed to build upon the machine learning concepts introduced in Machine Learning, Modeling, and Simulation Principles.
This course delves into the practical applications of machine learning across various scientific and industrial domains, showcasing how machine learning techniques address critical challenges in these fields.
Ideal for scientists, researchers, engineers, and anyone seeking to enhance their ability to model and simulate systems, this course offers insights into generating and refining data to make informed decisions. Unlike many machine learning courses focusing on consumer behavior prediction or text analysis, this course is tailored to engineering and science applications, which often have distinct requirements.
Proficiency in several areas of undergraduate-level mathematics is essential for this course, along with familiarity with MATLAB, although prior programming experience is not mandatory. For more details, please refer to this FAQ article.
The course curriculum features interactive practice exercises with immediate feedback and graded assignments utilizing guided MATLAB code, enabling learners to explore and develop an understanding of machine learning methods, their strengths, and their limitations. Moreover, presentations from MIT faculty across various fields, including chemical, mechanical, and aerospace engineering, as well as geological and material sciences, offer insights into applying machine learning to solve real-world problems in diverse industries.
Each week of the course focuses on specific applications of machine learning:
- Feature Engineering: Predicting lithium-ion battery life and constructing non-linear models.
- Computational Imaging: Applications in compressive sensing, phase retrieval, and tomography.
- Seismic Imaging: Utilizing neural nets to generate missing data and imaging unreliable data.
- Oil and Gas Lease Prediction: Minimizing decision-making risk using generalized linear regressions.
- Geometric Data Processing: Summarizing large datasets of point cloud and vector data using deep learning techniques.
As the course builds upon foundational principles, we recommend taking the Machine Learning program's courses sequentially. Learners with a master's degree in Engineering, familiarity with Linear Algebra, and recent work experience in a related field may consider taking Course 2 first. However, others are advised to begin with Course 1.