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UID:20250725T191132EDT-3715KOfSUi@132.216.98.100
DTSTAMP:20250725T231132Z
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
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