This course covers the underlying theories and algorithms used in the field of artificial intelligence. Topics include the history of AI, search algorithms (such as A*, evolutionary search, and constraint satisfaction), logic and logic programming, planning, and an overview of machine learning. Programming assignments, including implementation of AI algorithms, and presented /written summaries of research papers are required.

Graduate capstone project by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

We will explore the relationship between language and technology from the invention of writing systems to current natural language and speech technologies, and especially Large Language Models. Topics include script decipherment, machine translation, automatic speech recognition and generation, dialog systems, computational natural language understanding and inference, as well as language technologies that support users with language disabilities. We will also trace how science and technology are shaping language, discuss relevant artificial intelligence concepts, and examine the ethical implications of advances in language processing by computers. Students will have the opportunity to experience text analysis with relevant tools. This is an interdisciplinary course and technical background is not required.

This course provides theoretical foundation as well as hands-on (lab-style) practice in computational approaches for processing natural language text, for problems that involve natural language meaning and structure. The course has relevance to cognitive science, artificial intelligence, and science and technology fields. Large language models and machine learning, including standard and deep neural network methods, is a central component of this course. Students will develop natural language processing solutions individually or in teams using Python, and explore additional relevant tools and LLMs or related foundation models. Expected: Programming skills, demonstrated by coursework or instructor approval.