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Artificial Intelligence

The goal of this course is for students to gain an understanding of what artificial intelligence (AI) is and how intelligent systems are trained to efficiently perform specific tasks. This course will cover a variety of methods in modern artificial intelligence including supervised learning, unsupervised learning, and reinforcement learning algorithms. Students analyze the strengths and weaknesses of these algorithms and learn not only how bias enters data and algorithms, but also what can be done to mitigate this bias. Students will apply fundamental mathematical concepts involving linear algebra, statistics, calculus, and optimization. Students also develop programming proficiency through practice implementing algorithms in Python using both pedagogical and real-world datasets. Through both theory and practice, students learn a broad class of machine learning algorithms that they can apply to build safe, efficient, and ethical AI systems that solve relevant real-world problems.

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Session One
-
Session Two
-
Grade(s)
10-11
at the time of application
Scheduled Class Time*

08:00 AM - 11:00 AM (PDT)

Session One

04:00 PM - 07:00 PM (PDT)

Session One

08:00 AM - 11:00 AM (PDT)

Session Two

04:00 PM - 07:00 PM (PDT)

Session Two

*The course will meet for two hours daily (Monday–Friday) for a live online class during this window of time. The third hour is used for online office hours. Students will be admitted to and attend just one course section and time. The exact course time and office hour schedule will be set closer to the start of the program. In addition to the live meeting times, students complete out-of-class learning assignments such as assigned readings, group work, pre-recorded online lectures, and more.

Prerequisite(s)

Beginning proficiency with programming language Python.