Computer systems that process human language have become ubiquitous in modern life. This course surveys modern language technologies, which may include writing systems, writing assistants, language learning assistants, text classifiers, search engines, machine translation systems, and large language models such as ChatGPT. For the technologies covered, we explore how they function, emphasizing insights from linguistics, and discuss the ethical and social consequences of their use. No technical background is assumed.

In this course, students will develop their practical programming skills in Python while gaining familiarity with algorithms and data structures that are foundational for computational linguistics. Topics covered include linked lists, queues and stacks, tree-based data structures, tree-based algorithms, sorting algorithms, graphs, and search algorithms.

This course is geared towards students in Computational Linguistics and Linguistics, with little or no experience in programming. It introduces the fundamentals of programming and computer science, aiming at attaining practical skills for text processing. In contrast to similar courses in Computer Science, we will concentrate on problems in Computational Linguistics, which involve managing text, searching in text, and extracting information from text.

An introduction to machine learning (ML) methods for computational linguistics (CL). We begin by covering basic concepts in ML before turning to foundational ML methods in CL. Throughout, students develop practical skills in ML programming in Python, relying on libraries such as NumPy, PyTorch, and scikit-learn. Students will develop an understanding of the mathematical details underlying modern models used in CL, and will have practical skills for creating and using ML models for CL.

Large language models (LLMs) have dominated the attention of researchers in natural language processing and computational linguistics since the landmark releases of BERT (2018) and ChatGPT (2022). In this seminar, we broadly survey recent work on LLMs, covering their technical foundations, interpretation, applications, and ethical and philosophical considerations. Students will spend the bulk of their time working on a project of their choosing related to LLMs. By the end of the course, students will have an understanding of both the inner workings of LLMs and their broader significance in academia and society.