Advanced Data Mining (INFSCI 2165)
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FATE: Fairness, Accountability, Transparency, and Ethics in AI (INFSCI 2935)
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Data Mining (INFSCI 2160)
This course will introduce the core data mining concepts and practical skills for applying data mining techniques to solve real-world problems. Topics cover major data mining problems as different types of computational tasks (prediction, classification, clustering, etc.) and the algorithms appropriate for addressing these tasks, as well as systematic evaluation and model assessment. Students are expected to design and implement data mining applications using real-world datasets, and to leverage cloud-computing services to build big data analytics projects.
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Information Visualization (INFSCI 2415; cross-listed with LIS 2690)
Visualization is a way to explore, present and express meaning in data, so there is no visualization without data. This course aims to investigate what data presents and how to present data. It will introduce concepts, methods and procedures of data visualization, with emphasis on the creative process of organizing, visualizing, communicating and interacting information. Students are expected to design and implement visualization systems using real-world datasets, and evaluate the systems in practical scenarios.
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Seminar: Data-Driven Decision Making: Validity & Accountability (INFSCI 3350)
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 made possible both by voluminous data and new machine learning tools that can exploit complex structures and patterns in data. Can data-driven decisions be better? How can we know? When would data-driven decisions become harmful? How can we avoid? How can we evaluate and trust data-driven decisions? This course will emphasize challenges of establishing validity and accountability in data-driven decision making, and investigate multidisciplinary approaches relevant to these challenges. Students will be working on substantial academic readings and research projects (Prerequisites: students need to have basic knowledge of statistics, machine learning, a willingness to do interdisciplinary research, and be comfortable to quickly learn many new things.)

Seminar: Social Networks and Graph Analysis (INFSCI 3350)
Social networks (friendship, co-authorship), information networks (the Web) and communication networks (emails, tweets and retweets) play a central role in the transmission of information and are critical to the trade of many goods and services. This doctoral seminar course will examine modern techniques for capturing, modeling and understanding the structure and dynamics of real-world networks. It covers topics such as: theoretical and empirical background on social and economic networks, concepts used to describe and measure networks, with a particular emphasis on graph-based data mining techniques, such as network dynamics, information diffusion, community detection, link prediction, etc. Substantial reading and project will be required. (Prerequisites: Data mining, machine learning or relevant course are suggested. Basic courses in Linear Algebra, Probability and Statistics are required.)

Seminar: Data Science (INFSCI 3250)
This seminar course will discuss advance techniques and key ideas in state-of-the-art data science research, covering topics including data mining, network science, computational social science, business intelligence, health informatics, etc. Substantial reading and project will be required. (Prerequisite: data mining or relevant course; registration requires instructor's permission.)

Tutorials & Workshop