Enabling Machine Learning Education In The Time Of Pandemic
Brief Abstract
For a cutting-edge course such as Machine Learning, where high performance computing infrastructure is key to learning, no access to on-campus labs is a challenge. Join Barbra Sobhani as she shares how the RRCC ML course continued to thrive during this pandemic with the help of HP solutions.
Presenters
Extended Abstract
Data literacy and data sciences skills consistently rank in the top 1 or 2 of job skills in demand across industries. Community colleges are uniquely positioned to develop such skills for traditional students and lifelong learners to compete in the AI-powered workforce. In order to be successful, the college must be equipped with the proper computing infrastructure and be committed to evolving your curriculum in this fast-moving field. Through careful design of the infrastructure, machine learning is curriculum that has successfully made the adaptation to remote learning during the pandemic. By collaborating and building a community of experts among colleagues across campuses and industry partners we can increase student success and align skills with job demand in this data and AI-driven economy. Developing an introductory course that is adaptable to multiple modes of instruction is just the first step in the infusion of AI/ML across disciplines, critical for the community college mission of workforce development and accelerating the economic recovery.
Red Rocks Community College has over the past two years developed a combination of curriculum and an advanced computing infrastructure which has enabled AI/ML to be infused into a range of courses. The creation of a pilot Mathematics of Machine Learning course was key to testing the computing infrastructure for adaptation to curriculum. The course has been taught multiple semesters, working out the best methods for introducing machine learning to students. The course has not pre-requisites, so it has wide accessibility for students. As an introductory course, the students start with developing an understanding of what machine learning is and how it can be used. The curse reviews the basic of mathematical techniques that are critical for developing machine learning algorithms. Building on this foundation, students dive into to some basic coding using a scaffolded approach. The course incorporates data selection and preparation, and culminates with a group project and presentation. This course has been submitted for adoption by the State of Colorado Community College System as an exemplar to be adapted broadly. The structure of the course and the architecture of the enabling compute infrastructure is critical for student success. as well as examine. RRCC has partnered with HP to develop a standalone computing infrastructure to support the development of a data science program. Faculty at RRCC initially tested the efficacy by engaging students in machine learning projects. The Mathematics of Machine Learning course will form the core of a data science certificate, based on input from industry partners, and eventually an AS degree in Data Science.
Community colleges are well poised to provide the workforce training in the burgeoning AI/ML field. By the end of the presentation, attendees should understand the necessity of addressing computing infrastructure from the outset of program development. They will also understand the how to begin integrating ML projects into courses.