Fall 2026

COSC-4463/LING-4463 | AI, Language, and Interaction (was: Dialogue Systems)

Claire Bonial Upperclass Undergraduate & Graduate

AI, Language, and Interaction examines how intelligent systems learn, represent, and use language, with a focus on the technical foundations of chatbots, task-oriented dialogue systems, and emerging LLM-based interactive agents. Bridging computer science and linguistics, the course explores parallels between usage-based theories such as Construction Grammar and the probabilistic linguistic patterns large language models acquire from text, while also addressing the limits of current systems in reasoning, grounding, and embodiment. Students will study how dialogue systems interpret context, manage interaction, and support real-world communication, with attention to future directions including multimodal and human-robot dialogue. The course is project-based, and final projects may take either a computational or a linguistic orientation, from building dialogue systems to conducting data-driven analyses of language and interaction. No formal prerequisites are required, but students should have prior coursework or a substantive interest in computer science, linguistics, or both.

COSC-4550 | Information Retrieval

Nazli Goharian Upperclass Undergraduate & Graduate

Information retrieval is the identification of textual components, be them web pages, blogs, microblogs, documents, medical transcriptions, mobile data, or other big data elements, relevant to the needs of the user. Relevancy is determined either as a global absolute or within a given context or view point. Practical, but yet theoretically grounded, foundational and advanced algorithms needed to identify such relevant components are taught.

The Information-retrieval techniques and theory, covering both effectiveness and run-time performance of information-retrieval systems are covered. The focus is on algorithms and heuristics used to find textual components relevant to the user request and to find them fast. The course covers the architecture and components of the search engines such as parser, index builder, and query processor. In doing this, various retrieval models, relevance ranking, evaluation methodologies, and efficiency considerations will be covered. The students learn the material by building a prototype of such a search engine. These approaches are in daily use by all search and social media companies.

LING-2040/4400 | Computational Language Processing

Ethan Wilcox Undergraduate & Graduate

This course will introduce students to the basics of Natural Language Processing (NLP), a field that combines linguistics and computer science to produce applications, such as generative AI, that are profoundly impacting our society. We will cover a range of topics that form the basis of these exciting technological advances and will provide students with a platform for future study and research in this area. We will learn to implement simple representations such as finite-state techniques, n-gram models, and topic models in the Python programming language. Previous knowledge of Python is not required, but students should be prepared to invest the necessary time and effort to become proficient over the semester. Students who take this course will gain a thorough understanding of the fundamental methods used in natural language understanding, along with an ability to assess the strengths and weaknesses of natural language technologies based on these methods.

LING-4401/DSAN-5400 | Computational Linguistics with Advanced Python

Trevor Adriaanse Upperclass Undergraduate & Graduate

This course presents topics in Natural Language Processing (NLP) and Python programming for both text processing and analysis. The goal of this class is to explore both classical and modern techniques in NLP, with emphasis on hands-on application. We will examine topics such as text classification, model evaluation, nearest neighbors, and distributed representations. Applications include authorship identification, structured prediction, and semantic textual similarity, to name a few.

Programming topics include Python best practices, scientific computing libraries (e.g., NumPy, sklearn, etc.), exception handling, object-oriented programming, and more. By the end of this course, students will be able to program proficiently in Python, with enough comfort to reference software documentation and pseudocode to write sophisticated programs from scratch.

Requirements: Basic Python programming skills are required (for example satisfied by LING-4400, Computational Language Processing/Intro to NLP)

LING-4427 | Computational Corpus Linguistics

Amir Zeldes Upperclass Undergraduate & Graduate

Digital linguistic corpora, i.e. electronic collections of written, spoken or multimodal language data, have become an increasingly important source of empirical information for theoretical and applied linguistics in recent years. This course is meant as a theoretically founded, practical introduction to corpus work with a broad selection of data, including non-standardized varieties such as language on the Internet, learner corpora and historical corpora. We will discuss issues of corpus design, annotation and evaluation using quantitative methods and both manual and automatic annotation tools for different levels of linguistic analysis, from parts-of-speech, through syntax to discourse annotation. Students in this course participate in building the corpus described here: https://corpling.uis.georgetown.edu/gum/

LING-4480 | Computational Linguistics Research Methods

Nathan Schneider Upperclass Undergraduate & Graduate

Computational Linguistics is a fast-growing and fast-moving field. This course is intended to give advanced undergraduate and graduate students practice conducting original research in computational linguistics and to enhance their research and communication skills. It will serve as a platform for students to pursue an independent research project with guidance and oversight from faculty and peers. Students will be expected to bring their own pre-existing research topics/questions to the class. Over the course of the semester, they will select and present key research papers pertinent to their topic, and develop the project with the goal of writing an ACL-style conference proceedings paper. In addition to hands-on research, this class will provide a venue for students to learn CL-related skills that often fall through the cracks of other, content-focused courses. Possible workshop topics include data annotation, LaTeX, and working with pretrained language models, as well as communication skills such as poster and slide design. As a hands-on course whose content changes based on the instructor and students, this course can be repeated for credit.

LING-8430 | Information, Structure and Language

Ethan Wilcox Upperclass Undergraduate & Graduate

This seminar brings together two divergent perspectives on human language. On one hand, linguistics research seeks to describe the structures that underlie human communication systems, often using formal tools such as grammars and logics. On the other hand, research in computer science, in particular information theory, seeks to discover the optimal way to package and transmit information over a channel. This seminar will focus on the intersection between these two programs: To what extent are human languages optimized for efficient communication? Can structural features of human language, or human linguistic behaviors be analyzed using the toolkit developed for efficient information exchange? Topics covered will include the structure of the lexicon, the relationship between syntactic and statistical dependencies, pragmatic inferences, as well various language-processing phenomena. Students will gain experience reading and presenting research papers in this area, and implementing concepts from information theory in code.

Prerequisite knowledge: Students should be proficient in at least one programming language (Python or R), and familiar with basic concepts of probability theory and/or machine learning.

ICOS-7710 | Cognitive Science Core Course

Abigail Marsh & Elissa Newport Graduate

A seminar in which important topics in cognitive science are taught by participating Georgetown faculty from the main and medical campuses. Required for the Cognitive Science concentration, available for Ph.D. students in other programs with instructor permission. (Can be taken more than once for credit.)

Spring 2027

COSC-3460 | Introduction to Machine Learning

Mark Maloof Undergraduate

This undergraduate course surveys the major research areas of machine learning focusing on classification. Through traditional lectures and programming projects, students learn (1) to understand the foundations of machine learning, (2) to design and implement methods of machine learning, (3) to evaluate methods of machine learning, and (4) to conduct empirical evaluations of multiple methods of machine learning. The course compares and contrasts machine learning with related endeavors, such as statistical learning, pattern classification, data mining, and information retrieval. Topics include instance-based approaches, naive Bayes, decision trees, rule induction, linear classifiers, support vector machines, neural networks, ensemble methods, evaluation, and applications. Students complete five programming projects using Java. There are midterm and final exams.

COSC/LING-5402 | Empirical Methods in Natural Language Processing

Nathan Schneider Graduate

Systems of communication that come naturally to humans are thoroughly unnatural for computers. For truly robust information technologies, we need to teach computers to unpack our language. Natural language processing (NLP) technologies facilitate semi-intelligent artificial processing of human language text. In particular, techniques for analyzing the grammar and meaning of words and sentences can be used as components within applications such as web search, question answering, and machine translation.

This course introduces fundamental NLP concepts and algorithms, emphasizing the marriage of linguistic corpus resources with statistical and machine learning methods. As such, the course combines elements of linguistics, computer science, and data science. Coursework will consist of lectures, programming assignments (in Python), and a final team project. The course is intended for students who are already comfortable with programming and have some familiarity with probability theory.

COSC-6422/LING-8422 | Advanced Semantic Representation

Nathan Schneider Graduate

Natural language is an imperfect vehicle for meaning. On the one hand, some expressions can be interpreted in multiple ways; on the other hand, there are often many superficially divergent ways to express very similar meanings. Semantic representations attempt to disentangle these two effects by exposing similarities and differences in how a word or sentence is interpreted. Such representations, and algorithms for working with them, constitute a major research area in natural language processing.

This course will examine semantic representations for natural language from a computational/NLP perspective. Through readings, presentations, discussions, and hands-on exercises, we will put a semantic representation under the microscope to assess its strengths and weaknesses. For each representation we will confront questions such as: What aspects of meaning are and are not captured? How well does the representation scale to the large vocabulary of a language? What assumptions does it make about grammar? How language-specific is it? In what ways does it facilitate manual annotation and automatic analysis? What datasets and algorithms have been developed for the representation? What has it been used for? Representations covered in depth will include FrameNet (http://framenet.icsi.berkeley.edu), Universal Cognitive Conceptual Annotation (http://www.cs.huji.ac.il/~oabend/ucca.html), and Abstract Meaning Representation (http://amr.isi.edu/). Term projects will consist of (i) innovating on a representation's design, datasets, or analysis algorithms, or (ii) applying it to questions in linguistics or downstream NLP tasks.

COSC-6440 | Deep Reinforcement Learning

Grace Hui Yang Graduate

Deep Reinforcement learning is an area of machine learning that learns how to make optimal decisions from interacting with an environment. From the environment, an agent observes the consequence of its action and alters its behavior to maximize the amount of rewards received in the long term. Reinforcement learning has developed strong mathematical foundations and impressive applications in diverse disciplines such as psychology, control theory, artificial intelligence, and neuroscience. An example is the winning of AlphaGo, developed using Monte Carlo tree search and deep neural networks, over world-class human Go players. The overall problem of learning from interaction to achieve goals is still far from being solved, but our understanding of it has improved significantly. In this course, we study fundamentals, algorithms, and applications in deep reinforcement learning. Topics include Markov Decision Processes, Multi-armed Bandits, Monte Carlo Methods, Temporal Difference Learning, Function Approximation, Deep Neural Networks, Actor-Critic, Deep Q-Learning, Policy Gradient Methods, and connections to Psychology and to Neuroscience. The course has lectures, mathematical and programming assignments, and exams.

LING-2040/4400 | Computational Language Processing

Ethan Wilcox Undergraduate & Graduate

This course will introduce students to the basics of Natural Language Processing (NLP), a field that combines linguistics and computer science to produce applications, such as generative AI, that are profoundly impacting our society. We will cover a range of topics that form the basis of these exciting technological advances and will provide students with a platform for future study and research in this area. We will learn to implement simple representations such as finite-state techniques, n-gram models, and topic models in the Python programming language. Previous knowledge of Python is not required, but students should be prepared to invest the necessary time and effort to become proficient over the semester. Students who take this course will gain a thorough understanding of the fundamental methods used in natural language understanding, along with an ability to assess the strengths and weaknesses of natural language technologies based on these methods.

LING-4432 | Computational Psycholinguistics

Ethan Wilcox Upperclass Undergraduate & Graduate

What goes on inside your mind as you listen to a sentence? How is using language different from other types of thinking? Does an LLM process language in the same way as a person? Every day, you produce and comprehend thousands of words, mostly without conscious effort. What are the mechanisms that underlie this amazing human ability? In this class, we’ll explore what’s going on inside our minds during daily language use. Our primary toolkit for doing so will be computational modeling. Over the course of the semester, we’ll build a variety of models—including symbolic algorithms, statistical models, and neural networks—that can explain linguistic behaviors. Topics covered will include phonological perception, reading, language acquisition, and the differences between people and LLMs. Students will implement models in code and are expected to have a basic background in programming and statistics.

LING-4466 | Multilingual NLP

Kenton Murray Upperclass Undergraduate & Graduate

This is a project based course focusing on the design and implementation of systems that scale Natural Language Processing methods beyond English. The course will cover both multilingual and cross-lingual methods with an emphasis on zero-shot and few-shot approaches, as well as ‘silver’ dataset creation. Particular emphasis will be applied to low-resource NLP, dialectal variation, and under-resourced languages. Modules will include Cross-Lingual Information Extraction & Semantics, Cross-Language Information Retrieval, Multilingual Question Answering, Multilingual Structured Prediction, Multilingual Automatic Speech Recognition, as well as other non-English centric NLP methods. Students will be expected to work in small groups and pick from one of the modules to create a model based on state-of-the-art methods covered in the class. The course will be roughly two-thirds lecture based and one-third students presenting project updates periodically throughout the semester.

ICOS-7712 | Cognitive Science Seminar

Abigail Marsh Graduate

A seminar in which graduate students and faculty interested in the cognitive sciences will read and discuss prominent articles across our fields. Can be repeated for credit.