Dave Coffey
Instructional Designer,
University of Washington
How to Use AI and Machine Learning to Transform IT Services
Thursday, November 01 | 9:45AM–11:15AM MT | Meeting Room 501/502
Session Type:
Breakout Session
Delivery Format:
Lightning Round
Lightning Round 1: 3 Questions to Ask Before Engaging in AI Projects
In this session, we'll educate participants on the methods for evaluating applications of AI services in higher education. We will describe scenarios for when engaging in AI is appropriate to address institutional problems and conclude with some key questions to ask in this process to delineate successful use cases from unsuccessful ones early in the process.
Outcomes: Describe the types of institutional problems that can be addressed by AI * Successfully engage with AI application developers and vendors to address these problems * Articulate key questions to ask during this process as well as the answers that are associated with fruitful outcomes
Presenters: Barton Pursel, David Hellar and Drew Wham, The Pennsylvania State University
Lightning Round 2: Leveraging Machine Learning to Improve the Student Experience and Operations
Like many universities, the University of Washington has been challenged by the proliferation of technology on campus and increasing student expectations for high-performance, always-on network connectivity. In this session, hear how we are leveraging machine learning, analytics, and data-driven insights to proactively improve both the student experience and campus operations.
Outcomes: Understand how machine learning and advanced network analytics can help improve the student experience and IT operations * Learn how one university is utilizing these solutions * Determine how to best deploy these tools to solve your unique challenges
Presenters: David Coffey and David Morton, University of Washington
Lightning Round 3: Improving Support Services Through Applied Machine Learning
Learn how to use commercial and open-source applied machine learning technologies with support services data to better understand and improve the support experience. We will provide an overview of applied machine learning techniques, share workflows, and present case studies of how we use machine learning to improve the service experience.
Outcomes: Understand how to use applied machine learning techniques to gain new insights into operations data * Build a value case for investigating and developing machine learning capabilities within a support services operation * Develop a roadmap for incorporating machine learning into your operations
Presenters: Erik Hofer and Robert Jones, University of Michigan-Ann Arbor
In this session, we'll educate participants on the methods for evaluating applications of AI services in higher education. We will describe scenarios for when engaging in AI is appropriate to address institutional problems and conclude with some key questions to ask in this process to delineate successful use cases from unsuccessful ones early in the process.
Outcomes: Describe the types of institutional problems that can be addressed by AI * Successfully engage with AI application developers and vendors to address these problems * Articulate key questions to ask during this process as well as the answers that are associated with fruitful outcomes
Presenters: Barton Pursel, David Hellar and Drew Wham, The Pennsylvania State University
Lightning Round 2: Leveraging Machine Learning to Improve the Student Experience and Operations
Like many universities, the University of Washington has been challenged by the proliferation of technology on campus and increasing student expectations for high-performance, always-on network connectivity. In this session, hear how we are leveraging machine learning, analytics, and data-driven insights to proactively improve both the student experience and campus operations.
Outcomes: Understand how machine learning and advanced network analytics can help improve the student experience and IT operations * Learn how one university is utilizing these solutions * Determine how to best deploy these tools to solve your unique challenges
Presenters: David Coffey and David Morton, University of Washington
Lightning Round 3: Improving Support Services Through Applied Machine Learning
Learn how to use commercial and open-source applied machine learning technologies with support services data to better understand and improve the support experience. We will provide an overview of applied machine learning techniques, share workflows, and present case studies of how we use machine learning to improve the service experience.
Outcomes: Understand how to use applied machine learning techniques to gain new insights into operations data * Build a value case for investigating and developing machine learning capabilities within a support services operation * Develop a roadmap for incorporating machine learning into your operations
Presenters: Erik Hofer and Robert Jones, University of Michigan-Ann Arbor
Presenters
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Ben Hellar
Manager, Data Empowered Learning, The Pennsylvania State University -
Bob Jones
Executive Director, Emerging Technology and Support Services, University of Michigan-Ann Arbor -
David Morton
Director Retired - Networks & Telecommunications, University of Washington -
Bart Pursel
Chief Technology Officer, Unizin, Ltd. -
Drew Wham
Data Scientist, The Pennsylvania State University