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Introduction to Machine Learning: Classification and Clustering

square logo with white text on dark blue: UBC (University of British Columbia); below, with white text on lighter blue: Library Research Commons

Event Language

English

Format

virtual/virtuel

This workshop offers an exploration of machine learning models for clustering and classification. With the increasing availability of large datasets, these models play a crucial role in extracting valuable insights and making informed decisions. In this workshop, participants will gain insight into clustering algorithms such as K-means, explore popular classification algorithms like decision trees, and learn about anomaly detection. Through a combination of lectures and hands-on exercises, participants will learn how to preprocess data, select relevant features, and evaluate model performance. By the conclusion of the workshop, participants will have a solid foundation in building and deploying machine learning models for clustering and classification tasks.

In this workshop, we will use cloud-based platforms, so you don’t need to have Python installed. Please make sure that you have a Google Colaboratory (https://colab.research.google.com/) account. This workshop will involve hands-on exercises that require the use of programming tools and libraries commonly used in machine learning, such as Python and Scikit-learn. As such, prior familiarity with Python programming is recommended for participants to fully benefit from the practical component of the workshop.

Sign up for other Machine Learning workshops in this series:

Or view other upcoming Digital Scholarship workshops.  If participants attend all of “Special Topics in Digital Scholarship” series, it counts as six hours towards the cc:DH/HN certificate.

Date: Tuesday, March 26, 2024, 1:00pm – 3:00pm Pacific Time

Presenter: Vedant Bahel