Machine Learning with R: K-Means Clustering Online
This workshop will cover K-Means Clustering, a powerful machine-learning technique used for data segmentation and pattern recognition. K-Means is the most common clustering technique for unsupervised machine learning.
Workshop Learning Outcomes: Participants gain a practical understanding of how to use K-means to group data points, interpret cluster results, and apply clustering techniques to real-world datasets for insights and decision-making.
Details: Any preparatory work for the session can be found on its information page. This virtual workshop will be recorded and shared on the same page, and discoverable via the Sherman Centre's Online Learning Catalogue.
Facilitator Bio: Amirreza is a master's student in the Electrical and Computer Engineering department at McMaster University. He works as part of the DASH Team, providing data analytics consultations and conducting workshops in various domains of machine learning and programming. Engaged in the intricacies of the artificial intelligence domain, his focus lies in the realms of Computer vision, Statistical analysis and Large language models. He has a strong knowledge of Python and an understanding of other languages such as MATLAB and R. Deliberate and methodical, he approaches programming with a keen eye for detail, striving to develop algorithms that navigate the complexities of the field.
- Date:
- Friday, January 24, 2025
- Time:
- 4:00pm - 5:30pm
- Time Zone:
- Eastern Time - US & Canada (change)
- Online:
- This is an online event. Event URL will be sent via registration email.
- Audience:
- Everyone
- Categories:
- DASH SCDS Sponsored Events Workshops
CODE OF CONDUCT
The Sherman Centre and the McMaster University Library are committed to fostering a supportive and inclusive environment for its presenters and participants. As a participant in this session, you agree to support and help cultivate an experience that is collaborative, respectful, and inclusive, as well as free of harassment, discrimination, and oppression. We reserve the right to remove participants who exhibit harassing, malicious or persistently disruptive behaviour. Please refer to our code of conduct webpage for more information.