March 7, 2025 04:00 PM to 05:30 PM
Join us for a workshop on Random Forest. Random Forest is an ensemble machine-learning technique used for both classification and regression tasks. It is based on the concept of decision trees, where multiple decision trees are trained on different subsets of the data, and their predictions are combined to produce a more accurate and robust final prediction. Some applications of Random Forest Classification are in healthcare for diagnosing diseases based on patient data and in finance for detecting fraud by identifying unusual transaction patterns. It also finds applications in e-commerce for personalized recommendations and in environmental science for classifying land cover types from satellite imagery.
Workshop Learning Outcomes: This workshop will provide attendees with the ability to describe geographic data and the basic capabilities of geographic information systems. By completeing the workshop exercise, attendees will become familiar with four basic components of the geographic information systems software, QGIS: (1) the QGIS graphical user interface, (2) geospatial webservices, (3) coordinate reference systerms, and (4) the QGIS print layout tool.
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.
