New York University 2 credits Computer Science
This course offers a graduate-level introduction to the computational and statistical foundations of modern Artificial Intelligence. It equips students with a clear grasp of key ideas such as optimization, generalization, data shift, evaluation metrics, and robustness; the principles that determine how AI systems learn, perform, and sometimes fail. The course equips students not only to use modern AI tools effectively but also to look under the hood - understanding how data quality, objectives, context, and the needs of end users shape real-world outcomes. The course is designed to be hands-on and engaging, combining coding exercises, practical labs, and team-based projects with examples drawn from engineering and other domains at NYU. Students will learn not only how core AI concepts connect to real deployment challenges but also how to frame problems with stakeholders in mind and design solutions that are useful in practice. By the end of the course, students will be able to think critically and computationally about how to design AI systems responsibly and collaboratively. As a graduate course, students will engage deeply with model evaluation, robustness, and deployment tradeoffs, including readings from current research literature and project expectations emphasizing methodological rigor and system-level reasoning. Knowledge of programming experience equivalent to CS-UY 1113 or CS-UY 1114. This includes the ability to write and debug code, use functions and control flow, work with arrays/lists and basic data structures, and implement simple data-processing tasks. Familiarity with Python and basic numerical computing is recommended.
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