Event box
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.
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Location Details
- Location:
- *ONLINE*
If you have any questions, concerns, or accessibility needs please email eka.grguric@ubc.ca.
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- Date:
- Wednesday, July 10, 2024
- Time:
- 1:00pm - 3:00pm
- Audience:
- All Faculty Graduate
- Categories:
- Digital Scholarship Research Commons
- Presenter(s):
- Aisha Eldeeb