IS 557

Fall 2026 Part of Term 1

Part of Term 1
Aug 24-Dec 9

Applied Machine Learning: Team Projects

Credit: 4 hours.

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. Prerequisite: Students should have demonstrated ability, and must have taken one of the following courses, IS 577 (formerly IS 590 DT), IS 517 (formerly IS 590 MD), CS 412, CS 446 or a course demonstrably equivalent.

This course satisfies the General Education Criteria in Fall 2022 for:

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IS 557 class schedule data for fall 2026
	Status		CRN	Type	Section	Time	Day	Location	Instructor	Section Details
			75870	Lecture-Discussion	AC	10:00AM -12:50PM	R	428 Armory	Wang, H	Availability: Open (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Degree Notes: ONL Info Science rate course. Section Info: 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 Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to PHD:Library & Infor Sci -UIUC, PHD:Information Sciences -UIUC, MS:Bioinformatics: IS - UIUC, MS:Information Management-UIUC, or MS: Information Mgt Onl -UIUC. Restricted to students in the Illinois Informatics Institute or Information Sciences department.