Program: MSIS/PhD
Catalog Description
Introduction to Explainable AI through practical applications and real-world examples. Students will gain a basic proficiency in implementing Explainer Algorithms to explain the decisions of ML and AI systems, as well as interpreting the results in a transparent and understandable manner to a non-technical audience. The course will focus on Post-Hoc Explainability techniques and algorithms, including Feature Attribution, Rule-Based and Counterfactuals. Another focus area will be on evaluating the performance of explainability techniques, and understanding the trade-offs associated with various methods. This course is geared towards students interested in a hands-on approach to developing explanations for black-box ML and AI Systems.
Prerequisites
INF 380P Intro to Programming or equivalent experience with Python.
Scheduled and Upcoming Classes for this Course
No upcoming classes are scheduled for this course.
Past Classes for this Course
Class Name | Semester | Day(s) | Start Time(s) | End Time(s) | Building | Room |
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INF 385T: Special Topics in Information Science: Rapid Prototyping and Lean UX Methodology
Michael Mcquaid Syllabus |
Fall Term 2022 |
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