Principles of Data Science II

Catalog description

Principles of Data Science II builds upon the concepts introduced in Data Science I and shifts focus toward machine learning. In this course, we will cover the principles of machine learning in the domains of supervised learning, unsupervised learning, and reinforcement learning. We will illuminate these principles in terms of their mathematical foundations, their implementation in code, and practical applications. Specifically, we cover classical prediction and classification methods such as random forests or support vector machines as well as neural network approaches to these problems. Students will tie what they learned in this class together in a capstone project that incorporates these methods. Finally, we aim to touch on current developments in machine learning, such as generative AI. Formerly titled Principles of Data Science (the content of the course has not changed). Only open to students who intend to major or minor in Data Science or to major in either Computer and Data Science or Data Science and Mathematics. (All other students may enroll in DS-UA 100 , Discovering Data Science in the Age of AI.)