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Nov 24, 2024
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Rensselaer Catalog 2024-2025
Machine Learning and Artificial Intelligence Graduate Certificate (Rensselaer at Work)
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The Machine Learning and AI certificate prepares engineers and scientists to build analytic models that can predict/recognize based on environmental observations. The certificate also prepares students to construct decision-making systems based on predictions, toward intelligent actors. The program uses applied projects to build the ability to train systems, choose machine learning and tuning procedures, make predictions, and frame intelligent decision trees.
Students in the program work on a series of applied projects that closely resemble the activities a professional in the field works on, such as: design and deployment of analytical systems that serve as the basis for the analysis, processing, storage, and interface of the machine learning process; choose learning models appropriate to the result desired; developing predictive models that lead to the least likelihood of unintended variance; and build natural language and recommendation engines for common applications such as sales enhancement engines. Students observe results and tune recommendation models to achieve more accurate predictions and recommendations.
The program is taught by Rensselaer’s outstanding and highly ranked faculty. The program is built using a blended delivery methodology, bringing the best aspects of classroom and online learning together to deliver a unique project-based program – completely digitally, meaning that working professionals anywhere can participate fully in what is a personal and challenging experience. Not only will students learn the latest aspects of the profession, they also gain experience and proficiency through the projects.
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Certificate Information
The certificate requires three 3-credit hour courses, for a total of 9 credit hours.
- The program results in a graduate-level certificate in Machine Learning and Artificial Intelligence.
- The certificate’s blended online delivery reflects today’s dynamic workplace.
- Courses are taken for credit, and successful completion requires a GPA of 3.0 or greater.
- Practitioner faculty guide the completion of project work as it relates to student’s career objectives.
- As each of the courses build upon prior courses, courses must be taken in sequence.
- Students may choose to complete multiple certificates or apply certificates to other degree programs, according to each program’s requirements (for eligible programs, see program Web site at ewp.rpi.edu).
- For more information about start dates and application requirements, see program Web site at (ewp.rpi.edu).
Students completing this certificate are able to:
- use data in an analytic framework to evaluate and respond, in context, to a problem, decision, or research question.
- communicate with purpose and clarity in written and oral formats.
- develop predictive models based on regression and classification that lead to the least likelihood of unintended variance.
- develop predictive model driven recommendation engines for common applications such as sales enhancement.
- analyze data with regression, decision trees, and discriminant analysis to discover relationships and deliver insights to inform a problem, decision, or research question.
- observe results and then tune recommendation models with resampling and data splitting to achieve more accurate predictions and recommendations.
Curriculum
The following courses are required and are taken in sequence for a total of 9 credit hours:
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