BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250712T172256EDT-8814z5Ickt@132.216.98.100 DTSTAMP:20250712T212256Z DESCRIPTION:[This is session 3 in the series  Introduction to Machine Learn ing in Python]\n\nOverview:\n This workshop will focus on unsupervised mach ine learning and data cleaning. Unsupervised machine learning is a powerfu l technique where the algorithm analyzes and clusters unlabeled datasets. This workshop will scratch the surface of this side of machine learning\, introducing unsupervised learning using the k-means and DBSCAN algorithms. This session will explore the data cleaning process in the machine learni ng pipeline in more detail.\n\nBy the end of the workshop\, participants w ill be able to:\n    - Differentiate between supervised and unsupervised le arning\;\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 the steps in data cleaning\, along with the common issues a nd solutions to incomplete or faulty datasets.\n\nPrerequisites:\n    - Par ticipants should already have some familiarity with Python programming fun damentals\, e.g. loops\, conditional execution\, importing modules\, and c alling functions. Furthermore\, participants should ideally have attended the first lesson in the “Fundamentals of Machine Learning in Python” serie s\, or they should already have some background on the general machine lea rning pipeline.\n\n\nDate: Friday\, 24 March 2023.\n Time: 10 a.m. to 12 p. m.\n Location: hybrid (in-person at Burnside Hall 1104\, and online via Zoo m).\n Instructors: Jacob Errington\, Faculty Lecturer\, and Eric Mayhew\, g raduate student\, School of Computer Science\, ϲ University.\n\n\n \n \nRegister\n DTSTART:20230310T150000Z DTEND:20230310T170000Z SUMMARY:Unsupervised clustering and data cleaning URL:/cdsi/channels/event/unsupervised-clustering-and-d ata-cleaning-346482 END:VEVENT END:VCALENDAR