Teaching

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CLIP faculty teach courses across Computer Science, the College of Information, Linguistics, Neuroscience and Cognitive Science, and the Robert H. Smith School of Business. Topics include natural language processing, machine learning, computational psycholinguistics, information retrieval, data science, multilingual AI, trustworthy AI, language-model interpretation, and the social impacts of AI. Course offerings and instructors change by semester.

Fall 2026 (scheduled)

• INST 447: Data Sources and Manipulation (Ai)
• INST 414 / SDSI 414: Data Science Techniques (Buntain)
• INST 425: AI for Text Analysis (Mendelsohn)
• INST 423 / INST 623: AI Adoption Clinic--Values-Centered AI for Community Organizations (Frías-Martínez)
• BMGT 406: AI Augmented App Development (Raschid)
• CMSC 422: Introduction to Machine Learning (Carpuat)
• CMSC 470: Introduction to Natural Language Processing (Rudinger)
• CMSC 723 / INST 735 / LING 723: Natural Language Processing (Iyyer)
• CMSC 848A: Trustworthy Machine Learning (Daume)
• CMSC 848R: Language Model Interpretability (Wiegreffe)
• LING 849C / CMSC 828F / NACS 728W: Computational Psycholinguistics (Feldman, Resnik)

Spring 2026

• INST 878: LLMs in Action--Transforming Information Research with GenAI (Ai)
• CMSC 848Q: Good AI Answers to Questions--How to Get Them and What Can Go Wrong (Boyd-Graber)
• INST 414 / SDSI 414: Data Science Techniques (Buntain)
• CMSC 848T: Frontiers in Multilingual AI (Carpuat)
• INST 664: Transforming Unstructured Content with AI (Mendelsohn)
• INST 423 / INST 623: AI Adoption Clinic--Values-Centered AI for Community Organizations (Frías-Martínez)
• LING 499R: Understanding Language Understanding (Resnik)
• CMSC 470: Introduction to Natural Language Processing (Rudinger, Wiegreffe)

Spring 2022

• How and Why Artificial Intelligence Answers Questions (Boyd-Graber)

Fall 2021

• Natural Language Processing (Boyd-Graber)

Spring 2021

• Computational Linguistics I (Boyd-Graber/Resnik)

Fall 2017

• Computational Linguistics I (Carpuat)
• Multidisciplinary Seminar (Resnik)
• Machine Learning (Boyd-Graber)

Spring 2017

• Computational Linguistics II (Resnik)

• Computational Psycholinguistics (Feldman)

Fall 2016

• Computational Linguistics I (Carpuat)
• Reinforcement Learning (Daume)
• Computational Models of Human Parsing (Resnik)

Spring 2016

• Computational Linguistics II (Resnik)
• Machine Learning (Daume)

Fall 2015

• Computational Linguistics I (Carpuat)
• Computational Social Science (Resnik)

Spring 2015

• Computational Linguistics II (Resnik)
• Computational Psycholinguistics (Feldman)
• Multilingual Natural Language Processing (Carpuat)

Fall 2014

• Computational Linguistics I (Daume)
• Semantics in Computational Linguistics (Resnik)

Spring 2014

• Linguistic Prediction (Daume, Feldman, Lau)
• Computational Linguistics II (Resnik)
• Digging into Data (Boyd-Graber)

Fall 2013

• Computational Linguistics I (Boyd-Graber)
• Computational Social Science (Resnik)

Spring 2013

• Bayesian Nonparametrics (Boyd-Graber, Daume, Feldman)
• Computational Linguistics II (Resnik)
• Digging into Data (Boyd-Graber)
• Machine Learning (Daume)
• Cloud Computing (Lin)
• Computational Psycholinguistics (Feldman)

Fall 2012

• Computational Linguistics I (Daume)

Spring 2012

• E-Discovery (Oard)
• Computational Linguistics II (Boyd-Graber)
• Computational Psycholinguistics (Feldman)

Fall 2011

• Computational Linguistics I (Hollingshead)
• Multilingual Modeling (Daume)

Spring 2011

• Computational Linguistics II (Resnik)
• Computational Psycholinguistics (Feldman)

Fall 2010

• Computational Linguistics I (Daume)
• Seminar in Computational Linguistics (Resnik)
• Machine Learning (Getoor)

Spring 2010

• Computational Linguistics II (Resnik)
• Cloud Computing (Boyd-Graber)