INFSCI 2935 SPECIAL TOPICS: COGNITIVE
DescriptionMinimum Credits: 3
Maximum Credits: 3
An independent study intended to cover advanced material outside of or beyond the scope of current course offerings, specifically within the Cognitive Science or Cognitive Systems academic area.
Academic Career: Graduate
Course Component: Independent Study
Grade Component: Grad LG/SNC Basis
Course Requirements: PROG: School of Information Science or Sch Computing and Information; PLAN: Excluded Plans = Library & Information Science (MLIS, PHD, CERT-Advanced)
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|31437 (1050)||Tu||1:15 pm - 4:05 pm||WEB||Y. Lin||IND||AT|
|Description||TOPIC: Fairness in Machine Learning Data-driven models have been increasingly used in many domains to assist in human decision-making that has a significant impact on people’s lives – from job hiring and promotion, college admission, judicial decision, to business or public service delivery. The development of decision aids has been made possible both by voluminous data and new data science tools that can exploit complex structures and patterns in data. This course focuses on both concepts and practice in order to understand and cope with the ethical challenges in data science and data-driven decision making. We will introduce (a) the core concepts of fairness and interpretability mechanism and (b) analytic and technical tools to mitigate emerging problems in the real world. The objective of this course is to prepare future data scientists with the competence to recognize where and understand why (un)fairness and ethical issues arise when applying data science to real-world problems, learn how to conceptualize, measure, and mitigate bias in data-driven decision-making, learn how to evaluate models and make data-driven decision-making more interpretable and explainable, and learn to think critically about data-driven decisions and policy questions and evaluate a project with these concerns in mind. Students are expected to be familiar with the basics of Probability and Statistics, Data Mining/Machine Learning, and should be comfortable with programming with DM/ML toolkits. Students need to have a willingness to do interdisciplinary research and be comfortable to learn concepts through reading technical, legal, and policy documents.|
|28987 (1050)||W||12:10 pm - 3:00 pm||WEB||C. Lewis||IND||AT|
|Description||HUMAN ROBOT INTERACTION (HRI)|