BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250724T182709EDT-6132C1Unz6@132.216.98.100 DTSTAMP:20250724T222709Z DESCRIPTION:Machine Learning in Python - ML overview and k-nearest neighbou rs algorithm\n\nOverview: Nowadays\, machine learning (ML) is perhaps the hottest topic in all Computer Science\, and with good reason: the variety of tasks that can be completed by machine learning models has exploded in the last 15 years as compute power has reached new heights. But what exact ly is a “machine learning model”? This workshop will introduce you to the basic terminology and concepts associated with machine learning in a hands -on way. We will explore common ML tasks such as data acquisition and clea ning as well as model training\, testing\, and validation by focusing on a particularly simple kind of model called k-nearest neighbours.\n\nLearnin g Goal(s): By the end of the workshop\, participants will be able to:\n\n \n Describe at a high level what machine learning is and how it works\, the uses and applications of machine learning\, as well as its limitations an d ethical considerations.\n Describe the machine learning pipeline\, consis ting of data acquisition\, data cleaning\, algorithm selection\, training\ , testing\, and validation.\n Explain in plain English how the following al gorithm works: k-nearest neighbours\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.\n\n Approach: Our approach is primarily student-centered. Students will work i n pairs and small groups on worksheets and Jupyter notebooks\, intersperse d with brief lecture and instructor-led live-coding segments.\n\nSupportin g Resources: We will refer to many of the materials used previously in our series “Computing Workshop” https://computing-workshop.com/\n\nDeliverabl es: Our resources will be made available via our web site.\n\nResources re quired: Participants should have access to a laptop computer. Python shoul d be already installed with Anaconda.\n\nLocation: HYBRID at the McIntyre Medical Building\, room 325\, and via Zoom.\n Instructors: Jacob Errington\ , Faculty Lecturer in Computer Science at ϲ University. Eric Mayhew\, Computer Science professor at Dawson College.\n\nRegistration: Register H ere\n DTSTART:20241003T140000Z DTEND:20241003T160000Z SUMMARY:Workshop: Machine Learning in Python - Session 1 URL:/cdsi/channels/event/workshop-machine-learning-pyt hon-session-1-360401 END:VEVENT END:VCALENDAR