Introduction to Machine Learning: Classification and Clustering

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:

• Introduction to Machine Learning: Neural Networks

Or view other upcoming Digital Scholarship workshops.

Location Details

Location:

*ONLINE*

If you have any questions, concerns, or accessibility needs please email eka.grguric@ubc.ca.

To keep up-to-date with all of the workshops, consults, and events subscribe to the UBC Library Research Commons monthly newsletter.