Carnegie Mellon University
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Breadth Courses

Ph.D. students must take one course from three out of four Breadth areas. The courses that satisfy each Breadth requirement are shown below.

Note: Some Breadth courses may also fulfill the Application Engineering requirement. A single course is allowed to simultaneously fulfill a Breadth requirement and also the Application Engineering requirement.

Courses in this category provide grounding in how humans communicate and interact with other humans. Students learn to investigate significant questions in linguistics, social sciences, and related fields. An essential component is study of how humans communicate and interact with other humans.

Course Title Units Semester
11-722 Grammar Formalisms 12 Spring
11-724 Human Language for Artificial Intelligence 12 Fall
11-823 ConLanging: Learning Linguisics & Language Technologies via Construction of Artificial Languages 12 Spring
11-824 Subword Modeling 12 Spring
11-830 Ethics, Safety, and Social Impact in NLP and LLMs 12 Spring
80-683 Language in Use 12 Varies
80-788 Acoustics of Human Speech: Theory, Data, & Analysis 12 Varies

Courses in this category teach fundamental tasks and techniques in text-based natural language processing.

Course Title Units Semester
11-611 Natural Language Processing 12 Fall/Spring
11-697 Introduction to Question Answering With Large Language Models 12 Fall

11-711

 Advanced Natural Language Processing 12 Fall/Spring
11-737 Multilingual NLP 12 Uncertain (last offered F23)
11-797 Question Answering 12 Spring

Courses in this category transcend just text, teaching techniques for understanding and generating non-textual communication and interaction, for example, audio, speech, motion, image, and/or video.

Course Title Units Semester
11-692 Speech Technology for Conversational AI 12 Spring
11-751 Speech Recognition and Understanding 12 Fall
11-755 Machine Learning for Signal Processing 12 Fall
11-775 Large-Scale Multimedia Analysis 12 Fall
11-777 Multimodal Machine Learning 12 Fall/Spring
11-851 Talking to Robots 12 Fall

Courses in this category teach machine learning fundamentals for enduring questions in ML, including but not limited to optimization, statistical learning, and ML theory.

Course Title Units Semester
11-685 Introduction to Deep Learning 12 Fall/Spring
11-741 Machine Learning with Graphs 12 Fall
11-801 Quantitative Evaluation of Language Technologies 12 Fall
10-601 Introduction to Machine Learning 12

Fall/Spring
10-701 Introduction to Machine Learning 12 Fall/Spring
10-707 Advanced Deep Learning 12 Spring
10-708 Probabilistic Graphical Models 12 Fall/Spring
10-715 Advanced Introduction to Machine Learning 12 Fall
10-725 Optimization for Machine Learning 12 Fall/Spring