New York University 2 credits Computer Science
This course offers an undergraduate-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. The content is balanced for a range of majors and is intended to complement and prepare students for more advanced, upper-level AI-integrated courses. | Prerequisites: CS-UY 1113 or 1114 or equivalent Prerequisites: CS-UY 1113 or 1114 or equivalent.
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