Clustering and classification
Details
This 1-hour session introduces key concepts of clustering and classification, two fundamental techniques in data analysis and machine learning. Participants will explore the differences between supervised and unsupervised learning, understand how algorithms group data or assign class labels, and see practical examples of real-world applications. The session will include a brief theoretical overview followed by an interactive discussion of use cases, highlighting strengths, limitations, and best practices.
Agenda
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Speaker
Lida Hooshyar - University of Sherbrooke (PhD Student)
PhD Candidate in Computer Science
Phylogenetic supertrees reconstruction and analysis using topological approaches.
Hosted By
Nadia Tahiri, PhD, Organizer
Dr. Nadia Tahiri received the M.Sc. and Ph.D. degrees in Computer Science from the University of Quebec in Montreal, Canada. She was a Postdoctoral Researcher working on QSAR/PBPK model prediction in environmental health sciences at the University of Montreal, Canada. She has received several awards and scholarships, author of scientific works that have been published in prestigious journals, conferences, and book chapters. She is currently an assistant professor in the Department of Computer Science at the University of Sherbrooke. Her research interests include bioinformatics, phylogenetic tree, phylogeography, clustering, classification, computational biology, supertree, and consensus tree. Nadia is also very involved in community initiatives that promote women in technology and make committee programs more inclusive (the right measures for equity, diversity, and inclusion) as a committee scientific, an active member of ACM Canadian Celebration of Women in Computing, an ambassador of Women Techmakers, and an organizer of GDG Montreal (collaboration)/GDG Cloud Sherbrooke (lead).
Complete your event RSVP here: https://gdg.community.dev/events/details/google-gdg-cloud-sherbrooke-presents-clustering-and-classification/.