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Overview • Schedule • References • Office Hours • Team Project

Overview

Human-AI Interaction is an interdisciplinary area of computing concerned with interactivity in intelligent systems. This course serves as an introduction to the design and development of human-centered AI systems. Topics covered include machine intelligence, recommender systems, personalization and adaptivity, rationality and decision-making, agency and intiative, trust and transparency, anthropomorphism, human-computer interaction, interfaces and interaction design, GUIs, CUIs, mixed-initiative interaction, and human-AI teaming.

Schedule

Note: This schedule is subject to change.

Quick Links for Canvas: COSC494 // COSC594

Week	Date	Topic	Announcements
1	Jan 21	Introduction
2	Jan 26	Python Tutorial
	Jan 28	Recommender Systems
3	Feb 2	Team Project: Intro	TP: M1 Released
	Feb 4	Intelligent Interfaces	P1 Released
4	Feb 9	Team Discussions
	Feb 11	Architectures and Data
5	Feb 16	Team Discussions	P1 Due
	Feb 18	Machine Intelligence
6	Feb 23	Team Presentations	TP: M1 Due
	Feb 25	HCI and Mental Models	TP: M2 Released
			P2 Released
7	Mar 2	Team Discussions
	Mar 4	Evaluation Methods
		Guest Lecturer: Rhema Linder
8	Mar 9	Team Discussions	TP: M3/M4 Released
	Mar 11	Failure, Feedback, and Trust	P2 Due
9	Mar 16	Team Presentations	TP: M2 Due
	Mar 18	Prototyping and Language	P3 Released
10	Mar 23	Team Discussions
	Mar 25	Anthropomorphism
11	Mar 30	Team Discussions
	Apr 1	Decisions and Initiative	P3 Due
12	Apr 6	Team Presentations	TP: M3 Due
	Apr 8	Human-in-the-Loop Systems
13	Apr 13	Agents, Games, and the Web	P4 Released
	Apr 15	Black Mirror Day	HW1 Released
14	Apr 20	[ Team Project Time ] (No Class)
	Apr 22	The Future of HAI
15	Apr 27	Final Presentations	TP: M4 Due
			P4 Due
			HW1 Due

References

This course will make periodic reference to the following references and textbooks.

Primary Textbook References (All Optional)

• [SM] S. MacKenzie. Human-Computer Interaction: An Empirical Perspective. 2013.
• [HS] H. Simon. The Sciences of the Artificial, 3rd Edition. 1996.
• [WF] T Winograd and F. Flores. Understanding Computers and Cognition. 1986.

Publicly-Available Textbook References

• [CM] C. Molnar. Interpretable Machine Learning. 2020.
• [SB] S. Barocas, M. Hardt, and A. Narayanan. Fairness and Machine Learning. 2020.
• [HD] H. Daume III. A Course in Machine Learning. (In Progress)
• [BS] B. Settles. Active Learning. 2012.
• [RS] R. Sutton and A. Barto. Reinforcement Learning: An Introduction. 2018.
• [IG] I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. 2016.