Algorithmic Foundations for Artificial Intelligence

Catalog description

This course introduces students to essential algorithm design and analysis techniques with a focus on application in data science and machine learning. The course introduces students to elementary and advanced data structures to store and manipulate data. The course teaches different algorithm design and analysis techniques including classical paradigms such as divide and conquer, greedy, and dynamic programming, and advanced techniques such as randomized methods and algorithm tailored to machine learning such as gradient descent and online decision making. The course explores these techniques in context of different data types used in machine learning such as matrices, graphs and sequences. Students' learning of these concepts, techniques, and methods will be facilitated by exemplary programming demonstrations and coding exercises so that the students can utilize these algorithmic solutions in practical applications.