IS 590

Spring 2025 All Classes

All Classes

Advanced Topics in Information Foundations

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Credit: 2 OR 4 hours.

Variety of newly developed and advanced topics courses within the field of information foundations, intended to augment the existing Information Sciences curricula.

Additional fees may apply. See Class Schedule. May be repeated.

Class materials fee or field trip fee may be required.

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

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IS 590 class schedule data for spring 2025
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	69166	Lecture-Discussion	ML	9:30AM -12:20PM	T	37 Education Building	Bosch, N	Part of Term: 1 Date Range: 01/21/25-05/07/25 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Causal Inference Mach Learning Section Info: Causal inference is crucial in many scenarios, such as when offering interventions intended to improve education or when making policy decisions that are expected to cause certain changes. However, conducting a randomized controlled trial to answer every research question is often infeasible, or even unethical. This course will cover perspectives on causality and cutting-edge machine learning methods for causal inference in situations with non-linear effects, heterogeneous effects, large numbers of confounders, natural experiments, and even observational data. Students should have some previous stats experience (EPSY 580, IS 507, or similar, or approval of instructor). Students should also have familiarity with loading and manipulating datasets in R or Python. Restriction(s): Restricted to Graduate - Urbana-Champaign.