INF 385T: Special Topics in Information Science: Rapid Prototyping and Lean UX Methodology

Fall Term 2022
Mode: In Person
Program: MSIS/PhD
Unique ID
28555
Day Start End Building Room
  • Thursday
  • 3:30 PM
  • 6:30 PM
  • UTA
  • 1.208

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.

Restrictions

Restricted to graduate degree seekers in the School of Information during registration periods 1 and 2. Remaining seats will be made available to outside students on August 19th. Interested non-iSchool students may request a seat reservation by completing this Registration Support Questionnaire.