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DTSTAMP:20250428T015218Z
DESCRIPTION:Machine Learning in Python - Decision Trees & Data Acquisition
 \n\nOverview: Decision trees model sequential decision-making and can be u
 sed for both classification and regression tasks. This workshop will intro
 duce you to this type of machine learning model in a hands-on way: you wil
 l train a decision tree on a given dataset. We will discuss the pros and c
 ons of decision trees\, and see how using a random forest (a collection of
  decision trees) helps to address some of their shortcomings.\n\nLearning 
 Goal(s): By the end of the workshop\, participants will be able to:\n\n\n	D
 escribe with a diagram what a decision tree is.\n	Describe the advantages a
 nd disadvantages of decision trees as a machine learning algorithm.\n	Train
  a decision tree on a given data set for use in classification tasks\, fol
 lowing guidance from instructors.\n	Describe the data acquisition process i
 n machine learning and its ethical considerations\n\n\nPrereqs: Participan
 ts should already have some familiarity with Python programming fundamenta
 ls\, e.g. loops\, conditional execution\, importing modules\, and calling 
 functions. Furthermore\, participants should ideally have attended the fir
 st lesson in the “Fundamentals of Machine Learning in Python” series\, or 
 they should already have some background on the general machine learning p
 ipeline.\n\nApproach: 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 prev
 iously in our series “Computing Workshop” https://computing-workshop.com/
 \n\nDeliverables: Our resources will be made available via our web site.\n
 \nResources required: Participants should have access to a laptop computer
 . Python should be already installed with Anaconda.\n\nLocation: HYBRID. T
 he McIntyre Medical Building\, room 325\, and via Zoom.\n	Instructor: Jacob
  Errington\, Faculty Lecturer in Computer Science at �󷢲�Ʊƽ̨. Er
 ic Mayhew\, Computer Science professor at Dawson College.\n\nRegistration:
  Register Here\n
DTSTART:20241017T140000Z
DTEND:20241017T160000Z
SUMMARY:Workshop: Machine Learning in Python - Session 2
URL:/cdsi/channels/event/workshop-machine-learning-pyt
 hon-session-2-360402
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