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 develop mathematical proficiency through a fundamental understanding of the basics of 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.