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IS 557 AO - Appd Machine Learn: Team Proj

Campus: Urbana-Champaign


A comprehensive exploration of the applied machine learning workflow from inspiration to delivery of a machine learning solution broadly defined (i.e., from analytic finding to embedded machine learning application). This course is firmly grounded in a "learning-by-doing" teaching philosophy with pedagogical priority clearly placed on the application of machine learning to real-world data and problems. Ongoing and intense practical experiences in team-based project management and work are another cornerstone of this course. This course includes student-led reviews of existing data sources and machine learning technologies along with several team-based fact-finding and proof-of-concept implementation projects. This course is designed for students wishing to engage seriously in the practical world of machine learning implementation. Course Information: 4 graduate hours. No professional credit. Prerequisite: Students should have demonstrated ability, and must have taken one of the followi

Special Instructions:

In this course students will build upon their previously acquired skills in machine learning to undertake a variety of team-based projects which apply appropriate machine learning techniques to one or more real-world datasets to gain useful actionable insights. Teams will also document their analyses and findings, explaining the strengths, weaknesses, and reliability of their approaches. Advanced topics in machine learning will be discussed, but introductory topics will not be. Students are therefore expected to have previous experience in data mining/machine learning. Graduate student questions may be sent to ischool-advising@illinois.edu

Academic Program Restrictions:

PHD: Informatics - UIUC

Option 1

Number of Required Visit(s): 0

Course Level: Graduate

Credit: 4

Term(s): Spring


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