August 19 - August 21
Machine learning, also known as Artificial Intelligence or AI, is a statistical and algorithmic framework that has impacted materials science and manufacturing through its ability to model the hidden connections between observations and response. Targeted toward technical professionals and graduate students, this course will introduce the mathematical, statistical, and programming skills necessary to construct some of the most common machine learning algorithms, multivariate regression and neural networks, and to provide a foundation for evaluating the scope and applicability of machine learning for materials and manufacturing problems.
WHO SHOULD ATTEND?
- Materials researchers
- Industry professionals looking to incorporate machine learning models into a production environment
- Graduate students in manufacturing, materials science, and engineering
- Manufacturing engineers
- Research engineers
This course is built upon a background in statistics (probability distributions and likelihood), optimization (functional minimization/maximization) and basic python programming (packages/libraries, functions, classes, inheritance, and basic application programming interface usage). While these topics will be covered briefly in the class, a basic familiarity of each will greatly improve success. Course material will be presented using interactive Jupyter Notebooks; participants are strongly encouraged to have a laptop and updated installation of Anaconda Python 3.7 (https://www.anaconda.com/).