BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250401T200214EDT-8107xvNnoI@132.216.98.100 DTSTAMP:20250402T000214Z DESCRIPTION:Maching Learning in Python - Unsupervised learning and model va lidation\n\nOverview: This workshop will focus on unsupervised machine lea rning and model validation. Unsupervised machine learning is a powerful te chnique where the algorithm analyzes and clusters unlabeled datasets. This workshop will scratch the surface of this side of machine learning\, intr oducing unsupervised learning using the k-means and DBSCAN algorithms. Thi s session will explore the model validation process in the machine learnin g pipeline in more detail.\n\nBy the end of the workshop\, participants wi ll be able to:\n\n\n Differentiate between supervised and unsupervised lear ning\n Given a scaffolded environment and curated data set\, train a DBSCAN model and describe how this algorithm works at a high level\n Articulate t echniques used for model validation\n\n\nPrereqs: Participants should alre ady have some familiarity with Python programming fundamentals\, e.g. loop s\, conditional execution\, importing modules\, and calling functions. Fur thermore\, participants should ideally have attended the first lesson in t he “Fundamentals of Machine Learning in Python” series\, or they should al ready have some background on the general machine learning pipeline.\n\nAp proach: Our approach is primarily student-centered. Students will work in pairs and small groups on worksheets and Jupyter notebooks\, interspersed with brief lecture and instructor-led live-coding segments.\n\nSupporting Resources: We will refer to many of the materials used previously in our s eries “Computing Workshop” https://computing-workshop.com/\n\nDeliverables : Our resources will be made available via our web site.\n\nResources requ ired: Participants should have access to a laptop computer. Python should be already installed with Anaconda.\n\nLocation: HYBRID. The McIntyre Medi cal Building\, room 325\, and via Zoom.\n Instructor: Jacob Errington\, Fac ulty Lecturer in Computer Science at 91. Eric Mayhew\, Comp uter Science professor at Dawson College.\n\nRegistration: Register Here\n DTSTART:20241031T140000Z DTEND:20241031T160000Z SUMMARY:Workshop: Machine Learning in Python - Session 3 URL:/cdsi/channels/event/workshop-machine-learning-pyt hon-session-3-360403 END:VEVENT END:VCALENDAR