Introduction to Machine Learning
Presenter: Wonkyung Jang, Ph.D.
Larger datasets and use of predictive analytical techniques have advanced the conclusions being made with Family Science research. Trying to understand all possible algorithms or intricate patterns with large datasets can be time-consuming and cost prohibitive leaving nuanced patterns and relationships unnoticed. A solution to this challenge is using machine learning (ML) techniques to extract meaning from data. ML detects patterns in massive datasets and makes predictions based on what the computer learns from those patterns quicker than what researchers could do manually. As an emerging skill within the Family Science discipline, ML offers a powerful toolkit to extract deeper insights from complex datasets, uncovering nuanced patterns and relationships that may have previously gone unnoticed.
This webinar offers an exciting exploration of ML and its transformative applications in Family Science. Geared towards participants with an introductory level of knowledge in statistics, attendees will navigate through the fundamentals of ML principles, learn about real-world case studies demonstrating how ML have driven breakthroughs in Family Science, and be provided with hands-on guidance on processing, cleaning, managing, and analyzing data. The training begins with supervised ML algorithms including classification and regularized regression, tailored for labeled datasets. Following this, unsupervised ML algorithms, embracing clustering and network analysis for extracting patterns from unlabeled data will be discussed. Attendees will leave equipped with tangible skills to employ ML methodologies in their own research projects.
Specifically, attendees will leave this webinar with the ability to:
- Identify and differentiate between the main types of ML (supervised and unsupervised learning) and articulate their applications in the context of Family Science,
- Clean, manage, and visually explore datasets for ML applications in their research, as well as effectively select and engineer relevant features for analysis, and
- Assess, select, and implement appropriate ML models for their specific research questions in Family Science
It is recommended that all registrants have access to R and R studio prior to the webinar. You can download the software free at https://posit.co/download/rstudio-desktop/.
The views expressed by the webinar presenters are their own.
Approved for 1.5 hours of CFLE continuing education.
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About the Presenter
Wonkyung Jang, Ph.D., is an assistant professor in the Jeannine Rainbolt College of Education at the University of Oklahoma, specializing in early care and education (ECE) and data science. Dr, Jang is passionate about empowering professionals in human development and Family Science to embrace the power of “Big Data” to help children overcome developmental challenges, engage families in ECE, and tackle pressing social justice challenges. Dr. Jang received his doctoral degree in education from the University of North Carolina at Chapel Hill and a master’s degree in statistics and a graduate certificate in computational linguistics. Drawing from his extensive experience in teaching data science courses, Dr. Jang contributes invaluable expertise to this webinar.
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