Intro to Machine Learning and AI

This course will provide students with the applied skills and conceptual understandings necessary to reason about, critique, conceptualize and apply key artificial intelligence (AI) technologies to their domain. Specifically, this course will provide students with a high-level understanding of the essential algorithmic, logical, statistical and computing principles that drive the systems currently described as "artificial intelligence," including linear and logistic regression, penalized regression, random forests, support vector machines (SVMs), deep learning, natural language processing (NLP) and large-language models (LLMs). The approach of this course is interdisciplinary, and we will approach interacting with these tools on two levels. The first is to understand the basic principles, assumptions and tradeoffs that each system leverages to achieve its results. The second is a "use-modify-create" approach to interacting with these technologies in the Python programming language. To achieve this, a large portion of early assignments will be focused on building your applied Python programming skills so that they can be leveraged towards domain-relevant examples and problems in the latter half of the term.