Course Information - Language Technologies Institute - School of Computer Science

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Welcome to the listing and information directory for all courses that have ever been offered by the LTI. Courses are grouped in numerical order followed by summaries for each individual course below. Selecting a course number will take you directly to the appropriate listing for further information. For a list of courses currently being offered, please visit the Schedule of Classes on the Enrollment Services website.

This list includes several courses from outside of the LTI that are especially relevant to LTI students. Further information about these courses is available on the web pages of the departments that offer them.

Depending on a student's interests, electives may be taken from the LTI, other departments within SCS, the Tepper School of Business, the Statistics department, or the University of Pittsburgh.

Note: Some courses that were cross-listed in the past are not cross-listed now. If the course is not cross-listed now, it does not count as an LTI course.

Course	Title	Units	Semester
11-324	Human Language for Artificial Intelligence	12	Fall
11-344	Machine Learning in Practice	12	Fall/Spring
11-345	Undergrad Independent Study	3-12	Spring
11-390	LTI Minor Project - Juniors	12	All
11-411	Natural Language Processing	12	Fall/Spring
11-422	Grammar Formalisms	12	Spring
11-423	ConLanging: Learning About Linguistics and Language Technologies Through Construction of Artificial Languages	12	Spring
11-424	Subword Modeling	12	Spring
11-430	Ethics, Safety, and Social Impact in NLP and LLMs	12	Spring
11-441	Machine Learning with Graphs	9	Fall/Spring
11-442	Search Engines	9	Fall/Spring
11-443	Machine Learning for Text Analysis (Renamed as 11-441 since Spring 2015)	12	All
11-465	Special Topics: Digital Signal Processing	12	Intermittent
11-481	Generative AI for Biomedicine	12	Fall
11-485	Introduction to Deep Learning	12	Spring
11-490	LTI Minor Project - Seniors	12	All
11-492	Speech Technology for Conversational AI	12	Fall
11-590	LTI Minor Project - Advanced	12	All
11-601	Coding Boot-Camp	12	Fall/Spring
11-604	Python for Data Science I	6	Spring
11-605	Python for Data Science II	6	Spring
11-611	Natural Language Processing	12	Fall/Spring
11-623	ConLanging: Learning About Linguistics and Language Technologies Through Construction of Artificial Languages	12	Spring
11-624	Human Language for Artificial Intelligence	12	Fall
11-630	MCDS Practicum - Internship	0	Summer All
11-631	Data Science Seminar	12	Fall
11-632	Data Science Capstone	12	Fall
11-633	MCDS Independent Study	3-36	Fall/Spring/Summer
11-634	MCDS Capstone Planning Seminar	12	Spring
11-635	Data Science Capstone - Research	12	Fall
11-636	MSAII Independent Study	VAR	Fall/Spring
11-637	Foundations of Computational Data Science	12	Fall/Spring
11-641	Machine Learning for Text Mining (Cross listed with 11-741/11-441)	12	Fall/Spring
11-642	Search Engines	12	Fall/Spring
11-643	Machine Learning for Text Analysis (Renamed as 11-641 since Spring 2015)	12	Fall/Spring
11-651	Artificial Intelligence and Future Markets	12	Fall
11-654	AI Innovation	12	Fall
11-661	Language and Statistics	12	Fall
11-663	Applied Machine Learning	12	Fall/Spring
11-675	Big Data Systems in Practice	12	Spring
11-676	Big Data Analytics	12	Fall
11-681	AI Venture Studio	12	Spring
11-683	Biotechnology Outsourcing Growth	6-Mini	Spring
11-685	Introduction to Deep Learning	12	Fall/Spring
11-688	Computational Forensics and Investigative Intelligence	12	Spring
11-690	MIIS Directed Study	1-48	Fall/Spring
11-691	Mathematical Foundations for Data Science	12	Fall
11-692	Speech Technology for Conversational AI	12	Fall
11-693	Software Method for Biotechnology	6-Mini	Fall
11-695	AI Engineering	12	Spring
11-696	MIIS Capstone Planning Seminar	6	Spring
11-697	Introduction to Question Answering with Large Language Models	12	Fall
11-699	MSAII Program Capstone	36	Spring
11-700	LTI Colloquium	6	Fall/Spring
11-705	Introduction to Research in Language Technologies	6	Fall
11-711	Advanced Natural Language Processing	12	Fall
11-712	Lab in NLP	6	Spring
11-713	Advanced NLP Seminar	6	Intermittent
11-714	Tools for NLP	6	Intermittent
11-716	Graduate Seminar on Dialog Processing	6	Fall
11-717	Language Technologies for Computer Assisted Language Learning	12	Intermittent
11-718	Conversational Interfaces	12	Intermittent
11-719	Computational Models of Discourse Analysis	12	Spring
11-721	Grammars and Lexicons	12	Fall
11-722	Grammar Formalisms	12	Intermittent
11-723	Linguistics Lab	6	Fall/Spring
11-724	Human Language for Artificial Intelligence	12	Fall
11-725	Meaning in Language	12	Intermittent
11-726	Meaning in Language Lab (Self-Paced)	6	Fall/Spring
11-727	Computational Semantics for NLP	12	Spring
11-728	Advanced Seminar in Semantics	6	Intermittent
11-731	Machine Translation and Sequence-to=Sequence Models	12	Fall
11-732	Self-Paced Lab: MT	6	Fall/Spring
11-733	Multilingual Speech-to-Speech Translation Lab	6	Intermittent
11-734	Advanced Machine Translation Seminar	6	Intermittent
11-736	Graduate Seminar on Endangered Languages	6	Intermittent
11-741	Machine Learning with Graphs	12	Fall/Spring
11-742	Search Engines
11-743	Self-Paced Lab: IR	6	Fall/Spring
11-744	Experimental Information Retrieval	12	Intermittent
11-751	Speech Recognition and Understanding	12	Fall
11-752	Speech Generation	12	Intermittent
11-753	Advanced Laboratory in Speech Recognition	6	Spring
11-754	Project Course: Conversational Systems	6	Springl
11-755	Machine Learning for Signal Processing	12	Fall/Spring
11-756	Design and Implementation of Speech Recognition Systems	12	Intermittent
11-757	Advanced Topics: Statistical Modeling for Spoken Dialog Systems	12	Intermittent
11-761	Language and Statistics	12	Fall
11-762	Language and Statistics II	12	Intermittent
11-763	Inference Algorithms for Language Modeling	12	Spring
11-765	Active Learning Seminar	6	Intermittent
11-766	Large Language Models Applications	12	Spring
11-767	On-Device Machine Learning	12	Fall
11-768	AI Agents	12	Fall
11-772	Analysis of Social Media	12	Intermittent
11-775	Large-Scale Multimedia Analysis	12	Spring
11-776	Human Communication and Multimodal Machine Learning	12	Intermittent
11-777	Multimodal Machine Learning	12	Fall
11-780	Research Design and Writing	12	Intermittent
11-781	Generative AI for Biomedicine	12	Fall
11-782	Self-Paced Lab for Computational Biology	6-12	Fall/Spring
11-783	Self-Paced Lab: Rich Interaction in Virtual World	6	Spring
11-785	Introduction to Deep Learning	12	Fall/Spring
11-787	AI Cofounder: A Startup Builder's Guide	12	Fall/Spring
11-791	Design and Engineering of Intelligent Information Systems	12	Fall/Spring
11-792	Intelligent Information Systems Project	12	Fall/Spring
11-794	Inventing Future Services	12	Intermittent
11-795	Seminar: Algorithms for Privacy and Security	6	Intermittent
11-796	Question Answering Lab	6	Spring
11-797	Question Answering	12	Spring
11-801	Quantitative Evaluation of Language Technologies	12	Fall
11-805	Socio-technical Evaluations of Generative AI	12	Intermittent
11-811	Interdisciplinary NLP: Language Modeling in the Wild	12	Fall
11-821	Advanced Linguistics Seminar	6	Spring
11-823	ConLanging: Learning About Linguistics and Language Technologies Through 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
11-860	Quantum Computing Cryptography and Machine Learning Lab	12	Spring
11-866	Artificial Social Intelligence	VAR	Spring
11-868	Large Language Model Systems	12	Fall
11-884	AI & Emerging Economies	12	Fall
11-899	Summarization and Personal Information Management	12	Intermittent
11-904	Python for Data Science I	6	Fall
11-905	Python for Data Science II	6	Fall
11-910	Directed Research	1-48	All
11-920	Independent Study: Breadth	1-48	All
11-925	Independent Study: Area	1-48	All
11-927	MIIS Capstone Project	36	Fall
11-928	Masters Thesis I	5-36	All
11-929	Masters Thesis II	5-36	All
11-930	Dissertation Research	1-48	All
11-932	Teaching Experience	12	Fall/Spring
11-935	LTI Practicum	1-36	All
11-962	Introduction to Machine Learning	12	Fall
11-967	Large Language Models: Methods and Application	12	Fall
11-973	Foundations of Computational Data Science	12	Fall
11-977	Multimodal Machine Learning	12	Spring
11-999	Special Topics in Language Technology	VAR	Spring
	Courses taught by LTI faculty in other departments:
Course	Title
10-601 10-701	Machine Learning (can only count under one focus per student) - LTI PhD students must register for 10-701 for it to count towards their required 8 courses. LTI Masters students should register for 10-601.
15-750	Algorithms
15-780	Artificial Intelligence
15-883	Computational Models of Neural Systems

Course Descriptions

11-344 - Machine Learning in Practice
Description	Machine Learning is concerned with computer programs that enable the behavior of a computer to be learned from examples or experience rather than dictated through rules written by hand. It has practical value in many application areas. This class is meant to teach the practical side of machine learning for applications, such as mining newsgroup data, building adaptive user interfaces or building natural language processing applications. There will be a significant project focus, and when you have completed the course, you should be fully prepared to attack new problems using machine learning. While it will be essential to learn conceptually how machine learning algorithms work and interact with data, the emphasis will be on effective methodology for using machine learning to solve practical problems. This is about knowing how to conceptualize a problem, knowing how to represent your data, being able to interpret your results properly, doing an effective error analysis, and using the results of the error analysis to make strategic decisions about how to adjust the way you have set up your data and selected and tuned your algorithms. We will cover a wide range of learning algorithms that can be applied to a variety of problems. In particular, we will cover topics such as decision trees, rule based classification, support vector machines, Bayesian networks, clustering and neural networks. In addition to readings from the course textbook, we will have additional readings from research articles that will be announced ahead of time and distributed on Canvas. In the last third of the course, there will be an introductory and non-technical coverage of the main concepts in natural language processing (NLP) and advances in machine learning, covering topics such as neural networks and deep learning, word embeddings, large language models and some of their applications.
11-345 - Undergrad Independent Study
Description	None
11-390 - LTI Minor Project - Juniors
11-411 - Natural Language Processing
Description	This course is about a variety of ways to represent human languages (like English and Chinese) as computational systems, and how to exploit those representations to write programs that do neat stuff with text and speech data, like translation, summarization, extracting information, question answering, natural interfaces to databases, and conversational agents. This field is called Natural Language Processing or Computational Linguistics, and it is extremely multidisciplinary. This course will therefore include some ideas central to Machine Learning and to Linguistics. We'll cover computational treatments of words, sounds, sentences, meanings, and conversations. We'll see how probabilities and real-world text data can help through the development of Large Language Models (LLMs). We'll see how different levels interact in state-of-the-art approaches to applications like translation and information extraction. From a software engineering perspective, there will be an emphasis on rapid prototyping, a useful skill in many other areas of Computer Science.
Pre-Requisites	15-211 Fundamental Data Structures and Algorithms
Course Site
11-422 - Grammar Formalisms
Description	The goal of this course is to familiarize students with grammar formalisms that are commonly used for research in computational lingusitics, language technologies, and lingusitics. We hope to have students from a variety disciplines (linguistics, computer science, psychology, modern languages, philosophy) in order to cover a broad perspective in class discussions. Comparison of formalisms will lead to a deeper understanding of human language and natural language processing algorithms. The formalisms will include: Head Driven Phrase Structure Grammar, Lexical Functional Grammar, Tree Adjoining Grammar and Categorial Grammar. If time permits, we will cover Penn Treebank, dependency grammar, and Construction Grammar. We will cover the treatment of basic syntactic and semantic phenomena in each formalism, and will also discuss algorithms for parsing and generating sentences for each formalism. If time permits, we may discuss formal language theory and generative capacity. The course is taught jointly by the following faculty of the Language Technologies Institute: Alan Black Alon Lavie Lori Levin (main coordinator)
11-424 - Subword Modeling
Description	The goal of this course is to lead students to engage broadly with the existing NLP and computational linguistics research on subword modeling and develop new computational approaches to problems in morphology, orthography, and phonology. In addition to three other miniprojects, students will be expected to produce one piece of research that can be developed into a conference or workshop paper (though submission is not a course requirement). The paper should be suitable for the Phonology, Morphology, and Word Segmentation tracks of the *ACL conferences, the SIGMORPHON workshop, Coling, or LREC.
Pre-Requisites	11-411 or 11-611 or 11-711
11-430 - Ethics, Safety, and Social Impact in NLP and LLMs
Description	As language technologies have become increasingly prevalent, there is a growing awareness that decisions we make about our data, methods, and tools are often tied up with their impact on people and societies. This course introduces students to real-world applications of language technologies and the potential ethical implications associated with them. We discuss philosophical foundations of ethical research along with advanced state-of-the art techniques. Discussion topics include: - Philosophical foundations: ethical philosophies, history, medical and psychological experiments, IRB and human subjects, ethical decision making, AI alignment. - Bias, Misrepresentation, Alignment: algorithms to identify biases in models and data and adversarial approaches to debiasing. - Civility in communication: techniques to monitor trolling, hate speech, abusive language, cyberbullying, toxic comments. - Democracy and the language of manipulation: approaches to identify propaganda and manipulation in news, to identify fake news, political framing. - Privacy & security : algorithms for demographic inference, personality profiling, and anonymization of demographic and personal traits. - NLP for Social Good: Low-resource NLP, applications for disaster response and monitoring diseases, medical applications, psychological counseling, interfaces for accessibility. - Multidisciplinary perspective: invited lectures from experts in behavioral and social sciences, rhetoric, etc.
Course Site	https://maartensap.com/11830/
11-441 - Machine Learning with Graphs
Description	Graphs offer a natural way to represent complex relationships among objects of all kinds. Neural network learning with graphs has become important in both academic research and industrial applications. This course (for graduate and undergraduate students who meet the prerequisites) offers a mixture of fundamental concepts, algorithms, basic and advanced models, and broad applications, ranging from social popularity analysis and knowledge graph reasoning to deep learning for solving NP-complete problems.
Pre-Requisites	CS courses on data structures, algorithms and programming (e.g. 15-213), linear algebra (e.g. 21-241 or 21-341) and intro probability (e.g. 21-325) Intro Machine Learning (e.g., 10-701 or 10-601) and Algorithm Design and Analysis (e.g., 15-451) are not required but helpful
Course Site
11-442 - Search Engines
Description	This course studies the theory, design, and implementation of text-based search engines. The core components include statistical characteristics of text, representation of information needs and documents, several important retrieval models, and experimental evaluation. The course also covers common elements of commercial search engines, for example, integration of diverse search engines into a single search service ("federated search", "vertical search"), personalized search results, diverse search results, and sponsored search. The software architecture components include design and implementation of large-scale, distributed search engines.
Eligibility	This course is intended for undergraduates, although it is open to all students who meet the pre-requisites.
Pre-Requisites	This course requires good programming skills and an understanding of computer architectures and operating systems (e.g., memory vs. disk trade-offs). A basic understanding of probability, statistics, and linear algebra is helpful. Thus students should have preparation comparable to the following CMU undergraduate courses. 15-210, Parallel and Sequential Data Structures and Algorithms (required) 15-213, Introduction to Computer Systems (required) 15-451, Algorithm Design and Analysis (helpful) 21-241, Matrices and Linear Transformations or 21-341, Linear Algebra (required) 21-325, Probability (helpful) 36-202, Methods for Satistics & Data Science (helpful)
Website	http://boston.lti.cs.cmu.edu/classes/11-442/
11-481 - Generative AI for Biomedicine
Description	"Recent progress of Artificial Intelligence has been transforming the approaches of scientific research across various disciplines. Generative AI models, such as AlphaFold, have become indispensable tools in fundamental biomedical research. This course offers students an opportunity to explore the latest developments in generative AI applied to biomedicine. Topics include models and methods for the prediction of protein structure from sequences, characterization of genome functions and interactions, modeling of cellular structures and tissue organizations, single cell biology, and drug design. We will cover a variety of models, such as pre-trained biological foundation models, diffusion models, Monte Carlo methods, graph neural networks, etc. Through this course, students will gain a deep understanding of how generative AI can be leveraged to address complex challenges in biomedicine. Specifically, we have the following Learning Objectives: 1. Solid understanding generative AI models. 2. Comprehensive knowledge and indisciplinary thinking about generative AI application to key biomedical applications, e.g., protein structure, regulatory sequence design, cellular structure and function, and drug design 3. Critical analysis of research papers on generative AI methodologies and their applications to biomedicine. 4. Project-based learning and problem solving. "
11-485 - Introduction to Deep Learning
Description	Neural networks have increasingly taken over various AI tasks, and currently produce the state of the art in many AI tasks ranging from computer vision and planning for self-driving cars to playing computer games. Basic knowledge of NNs, known currently in the popular literature as "deep learning", familiarity with various formalisms, and knowledge of tools, is now an essential requirement for any researcher or developer in most AI and NLP fields. This course is a broad introduction to the field of neural networks and their "deep" learning formalisms. The course traces some of the development of neural network theory and design through time, leading quickly to a discussion of various network formalisms, including simple feedforward, convolutional, recurrent, and probabilistic formalisms, the rationale behind their development, and challenges behind learning such networks and various proposed solutions. We subsequently cover various extensions and models that enable their application to various tasks such as computer vision, speech recognition, machine translation and playing games. Instruction Unlike prior editions of 11-785, the instruction will primarily be through instructor lectures, and the occasional guest lecture. Evaluation Students will be evaluated based on weekly continuous-evaluation tests, and their performance in assignments and a final course project. There will be six hands-on assignments, requiring both low-level coding and toolkit-based implementation of neural networks, covering basic MLP, convolutional and recurrent formalisms, as well as one or more advanced tasks, in addition to the final project.
Pre-Requisites	15-112 and 21-120 and 21-241
11-490 - LTI Minor Project - Seniors
11-590 - LTI Minor Project - Advanced
11-604 - Python for Data Science I
Description	Students learn the concepts, techniques, skills, and tools needed for developing programs in Python. Core topics include types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations. Students get an introductory experience with several development environments, including Jupyter Notebook, as well as selected software development practices, such as test-driven development, debugging, and style. Course projects include real-life applications on enterprise data and document manipulation, web scraping, and data analysis.
11-605 - Python for Data Science II
Description	Students learn the concepts, techniques, skills, and tools needed for developing programs in Python. Core topics include types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations. Students get an introductory experience with several development environments, including Jupyter Notebook, as well as selected software development practices, such as test-driven development, debugging, and style. Course projects include real-life applications on enterprise data and document manipulation, web scraping, and data analysis.
Pre-Requisites	11-604
11-611 - Natural Language Processing
Description	This course is about a variety of ways to represent human languages (like English and Chinese) as computational systems, and how to exploit those representations to write programs that do neat stuff with text and speech data, like translation, summarization, extracting information, question answering, natural interfaces to databases, and conversational agents. This field is called Natural Language Processing or Computational Linguistics, and it is extremely multidisciplinary. This course will therefore include some ideas central to Machine Learning and to Linguistics. We'll cover computational treatments of words, sounds, sentences, meanings, and conversations. We'll see how probabilities and real-world text data can help through the development of Large Language Models (LLMs). We'll see how different levels interact in state-of-the-art approaches to applications like translation and information extraction. From a software engineering perspective, there will be an emphasis on rapid prototyping, a useful skill in many other areas of Computer Science.
Pre-Requisites	15-211 Fundamental Data Structures and Algorithms
Course Site
11-636 - MSAII Independent Study
Description	Independent study course for students in MSAII program. MSAII students only!
11-637 - Foundations of Computational Data Science
Description	This course provides an introduction to foundational concepts, learning material and projects related to the three core areas of Data Science: Computing Systems, Analytics and Human-Center Data Science. Students completing this class will be prepared for further graduate education in Data Science and/or Artificial Intelligence. Students acquire skills in solution design (e.g. architecture, framework APIs, cloud computing), analytic algorithms (e.g. classification, clustering, ranking, prediction), interactive analysis (Jupyter and R) and visualization techniques for data analysis, solution optimization and performance measurement on real-world tasks. Technologies used in this course include: Python, Pandas, Numpy, Scikit Learn, Pandas, PyTorch, JupyterLabs / Jupyter Notebook, Spacy, nltk, sentence-transformers, Azure ML Deployment, Beautiful Soup 4, selenium, matplotlib or seaborn, tqdm, gensim, scipy ( sparse and linear packages ), various packages in the Python standard library ( collections, requests, datetime, re, and a couple of others ), and pdfminer.
Course Site	https://mcds-cmu.github.io/11637/
11-641 - Machine Learning for Text Mining (Cross listed with 11-741/11-441)
Description	Fall/Spring This is a full-semester lecture-oriented course (12 units) for the PhD-level, MS-level and undergraduate students who meet the prerequisites. It offers a blend of core theory, algorithms, evaluation methodologies and applications of scalable data analytic techniques. Specifically, it covers the following topics: Link Analysis Social-media Analysis Web-scale Text Classification Learning to Rank for Information Retrieval Deep Learning for Text Mining Matrix Factoorization (with SVD, non-negative and probabilistic matrix completion) Stochastic gradient descent Statistical significance tests Notice that 11-741 and 11-641 are 12-unit courses for graduate students, but 11-441 is a 9-unit course for undergraduate students.Although the lectures are the same for all students, the workload differs by course. That is, the required course work in 11-441 is a subset of that in 11-641, and the work in 11-641 is a subset of that in 11-741. See the detailed distinctions in the Grading section. 11-741 is among the required courses for PhD candidates in the Language Technologies Institute. while 11-641 only counts as a master-level course. Graduate students can choose either 11-741 or 11-641, depending on their career goals and backgrounds. Undergraduate students should take 11-441; exception is possible if approved by the instructor.
Pre-Requisites	CS courses on data structures, algorithms and programming (e.g. 15-213), linear algebra (e.g. 21-241 or 21-341) and intro probability (e.g. 21-325) Intro Machine Learning (e.g., 10-701 or 10-601) and Algorithm Design and Analysis (e.g., 15-451) are not required but helpful
Course Site
11-642 - Search Engines
Description	This course studies the theory, design, and implementation of text-based search engines. The core components include statistical characteristics of text, representation of information needs and documents, several important retrieval models, and experimental evaluation. The course also covers common elements of commercial search engines, for example, integration of diverse search engines into a single search service ("federated search", "vertical search"), personalized search results, diverse search results, and sponsored search. The software architecture components include design and implementation of large-scale, distributed search engines.
Eligibility	This course is open to all students who meet the pre-requisites.
Pre-Requisites	This course requires good programming skills and an understanding of computer architectures and operating systems (e.g., memory vs. disk trade-offs). A basic understanding of probability, statistics, and linear algebra is helpful. Thus students should have preparation comparable to the following CMU undergraduate courses. 15-210, Parallel and Sequential Data Structures and Algorithms (required) 15-213, Introduction to Computer Systems (required) 15-451, Algorithm Design and Analysis (helpful) 21-241, Matrices and Linear Transformations or 21-341, Linear Algebra (required) 21-325, Probability (helpful) 36-202, Methods for Statistics & Data Science (helpful)
Website	http://boston.lti.cs.cmu.edu/classes/11-642/
11-661 - Language and Statistics
Description	The goal of "Language and Statistics" is to ground the data-driven techniques used in language technologies in sound statistical methodology. We start by formulating various language technology problems in both an information theoretic framework (the source-channel paradigm) and a Bayesian framework (the Bayes classifier). We then discuss the statistical properties of words, sentences, documents and whole languages, and the various computational formalisms used to represent language. These discussions naturally lead to specific concepts in statistical estimation. Topics include: Zipof's distribution and type-token curves; point estimators, Maximum Likelihood estimation, bias and variance, sparseness, smoothing and clustering; interpolation, shrinkage, and backoff; entropy, cross entropy and mutual information; decision tree models applied to language; latent variable models and the EM algorithm; hidden Markov models; exponential models and the maximum entropy principle; semantic modeling and dimensionality reduction; probabilistic context-free grammars and syntactic language models.
Course Site
11-663 - Applied Machine Learning
Description	Machine Learning is concerned with computer programs that enable the behavior of a computer to be learned from examples or experience rather than dictated through rules written by hand. It has practical value in many application areas. This class is meant to teach the practical side of machine learning for applications, such as mining newsgroup data, building adaptive user interfaces or building natural language processing applications. There will be a significant project focus, and when you have completed the course, you should be fully prepared to attack new problems using machine learning. While it will be essential to learn conceptually how machine learning algorithms work and interact with data, the emphasis will be on effective methodology for using machine learning to solve practical problems. This is about knowing how to conceptualize a problem, knowing how to represent your data, being able to interpret your results properly, doing an effective error analysis, and using the results of the error analysis to make strategic decisions about how to adjust the way you have set up your data and selected and tuned your algorithms. We will cover a wide range of learning algorithms that can be applied to a variety of problems. In particular, we will cover topics such as decision trees, rule based classification, support vector machines, Bayesian networks, clustering and neural networks. In addition to readings from the course textbook, we will have additional readings from research articles that will be announced ahead of time and distributed on Canvas. In the last third of the course, there will be an introductory and non-technical coverage of the main concepts in natural language processing (NLP) and advances in machine learning, covering topics such as neural networks and deep learning, word embeddings, large language models and some of their applications.
11-681 - AI Venture Studio
Description	A hands-on experience for gifted technologists to work with product and sales leaders to align their talents with purpose, generate impactful ideas, and deploy services in market with a venture mindset.
11-683 - Biotechnology Outsourcing Growth
Description	An especially dangerous time for new ventures is right after the initial product launch. At startup, many ventures run lean with a small headcount and minimal operational overhead. After some success, the startup is compelled to expand headcount, increase capital expansion, and scale up operations. In many cases, what was a promising theoretical business model may fail due to inadequate growth management. Biotechnology companies in particular are increasingly having key functions outsourced to reduce cost and increasing efficiency. The capital cost for laboratories and specialized lab technicians is often prohibitive for biotech startups with a clear and narrow focus. Biotech startups are therefore running much leaner but with a distributed organizational structure. Under these circumstances, managing outsourced functions becomes critical and is a focus of this course. This course will introduce students to issues with growth strategy and outsourcing management.
11-685 - Introduction to Deep Learning
Description	Deep learning is a subfield of AI that has lately taken the world by storm. Deep learning systems have been shown to able to recognize speech almost as well as humans, recognize images better than humans, read the web and answer questions, learn on their own to play games, beat humans at the toughest games like go and even speak more clearly than a human can. Deep learning currently dominates research in a variety of scientific areas, including text and language processing, data mining, speech processing, computer vision, robotics, and AI. Deep learning based products and services dominate the market in many areas. Whether youre using Google or social media, buying a plane ticket, browsing an online retailer, investing in stocks, or hailing an Uber, you are interacting with a deep learning system. Knowledge of deep learning is considered a valuable asset, and sometimes even essential, in the employment market. So what exactly is this mysterious beast? In this course we will study the basics of deep learning systems, starting from their humble beginnings as attempts to understand human cognition, their adolescence as artificial neural networks, leading on to the current complex systems that can perform astounding tasks. Students will learn both the underlying principles through a series of 13 lectures, and to actually implement and manipulate these systems for various tasks through a series of lab exercises.
11-688 - Computational Forensics and Invetigative Intelligence
Description	This course covers the use of computational methods in crime investigation (forensics) and prevention (intelligence). In almost all areas of forensics and intelligence, computational methods continue to aid, and sometimes entirely replace, human expertise in tracking crime. This is desirable since automation can address the problems associated with scale and global crime linkage through diverse data computational tools can potentially overcome and surpass human capabilities for crime investigation. This course is of a cross-disciplinary nature. It amalgamates knowledge from criminology, forensic sciences, computer science, statistics, signal processing, machine learning, AI, psychology, medicine and many other fields. Students from all departments and schools are welcome to take this course
Course Syllabus
11-690 - MIIS Directed Study
Description	to be determined by the department
11-691 - Mathematical Foundations for Data Science
Description	There is a familiar picture regarding software development: it is often delivered late, over-budget, and lacking important features. There is often an inability to capture the customer's actual way of accomplishing work, and then creating a realistic project plan. This will be especially important as software development in the life sciences involves creating applications that are relatively new to the industry. The course will introduce students to the "Balanced Framework" of project management process that assists biotechnology organizations in planning and managing software projects that support their product development. It provides the identification, structuring, evaluation and ongoing management of the software project that deliver the benefits expected from the organization's investments. It focuses on the delivery of business value being initiated by the project. It helps an organization answer the basic question "Are the things we are doing providing value to the business?" In this course, students will learn how to examine and explain customer processes and create requirements that reflect how work is actually done. Students will additionally create a software project plan that incorporates: problem framing; customer workflow, planning, project tracking, monitoring, and measurement.
11-693 - Software Method for Biotechnology
Description	Moore's law describes how processing power continues to be faster, better, and cheaper. It not only powered the computer industry forward, but it also is a key driver for propelling biotechnology. It is hard to imagine the world of biotechnology without the world of software. Moreover, the future will further underscore software's importance for enabling biotechnology innovations. This course is focusing on the relationship between biotechnology processes and information technology where students will be introduced to business process workflow modeling and how these concepts are applied in large organizations. Through this method, students will learn the key drivers behind information systems and how to identify organizational opportunities and leverage these to create disruptive models. Student will also learn to assess new technology sectors for unsolved problems and commercially viable solutions By taking this course, students will become conversant with the software technologies that can be applied to commercial life science problems in the present and future.
11-695 - AI Engineering
Description	The course takes a software engineering perspective on building software systems with a significant machine learning or AI component. It discusses how to take an idea and a model developed by a data scientist (e.g., scripts and Jupyter notebook) and deploy it as part of scalable and maintainable system (e.g., mobile apps, web applications, IoT devices). Rather than focusing on modeling and learning itself, this course assumes a working relationship with a data scientist and focuses on issues of design, implementation, operation, and assurance and how those interact with the data scientist's modeling. This course is aimed at software engineers who want to understand the specific challenges of working with AI components and at data scientists who want to understand the challenges of getting a prototype model into production; it facilitates communication and collaboration between both roles.
11-696 - MIIS Capstone Planning Seminar
Description	The MIIS Capstone Planning Seminar prepares students to complete the MIIS Capstone Project in the following semester. Students are organized into teams that will work together to complete the capstone project. They define project goals, requirements, success metrics, and deliverables; and they identify and acquire data, software, and other resources required for successful completion of the project. The planning seminar must be completed in the semester prior to taking the capstone project.
11-697 - Introduction to Question Answering with Large Language Models
Description	This course is designed to be accessible to Masters and advanced undergraduate students who seek the basic skills necessary to implement practical Question Answering (QA) applications using Large Language Models (LLMs) in specific information domains. The syllabus includes learning materials on the core concepts of QA and LLMs, and how they are applied in closed commercial systems (e.g. ChatGPT) as well as open systems (e.g. Llama, T5). Students complete a set of hands-on exercises in Python that develop skills in applying LLMs for various open-source QA datasets. The course is also a prerequisite for 11-797 Question Answering (an advanced project-oriented course).
11-699 - MSAII Program Capstone
Description	The final term will integrate all of the acquired learning in the program towards the development of a formal business plan and software product beta. The effort involved in the capstone project is quite intense and will consist of approximately three months of full time work for each student. The expected deliverables (features to be developed, business plan, technical documentation, etc.) must be agreed to by the course instructor at the outset of the course. The capstone can either encompass the development of an industry sponsored software project or a software product intended for entrepreneurial startup. Students are expected to showcase their business and software projects and elicit feedback from academics, industry professionals, investors, and business executives. This phase also acts as an incubation period for companies that will be launched from the program. This Course is for MSAII students only!
11-700 - LTI Colloquium
Description	The LTI colloquium is a series of talks related to language technologies. The topics include but are not restricted to Computational Linguistics, Machine Translation, Speech Recognition and Synthesis, Information Retrieval, Computational Biology, Machine Learning, Text Mining, Knowledge Representation, Computer-Assisted Language Learning and Intelligent Language Tutoring. To get credit of the course, students are required to write either a short critique of one of the presentations or a comparison of two.
Course Site	TBA
11-705 - Introduction to Research in Language Technologies
Description	This course is designed to introduce new LTI PhD/MLT students to the comprehensive landscape of research practices in language technologies, NLP, speech, etc. It aims to provide students with the necessary skills ranging from technical essentials (e.g., LTI cluster access), research methodologies (e.g., data visualization, statistical testing), to research communication (e.g., preparing talks) and professional development (e.g., conference networking). Through a combination of lectures, workshops, and hands-on sessions, students will learn to navigate the complexities of academic research, prepare for diverse professional settings, and contribute to a supportive and inclusive research community.Only LTI PhD and MLT students should register for this course.
11-711 - Advanced Natural Language Processing
Description	Advanced natural language processing is an introductory graduate-level course on natural language processing aimed at students who are interested in doing cutting-edge research in the field. In it, we describe fundamental tasks in natural language processing such as syntactic, semantic, and discourse analysis, as well as methods to solve these tasks. The course focuses on modern methods using neural networks, and covers the basic modeling and learning algorithms required therefore. The class culminates in a project in which students attempt to reimplement and improve upon a research paper in a topic of their choosing.
Topics	Introduction to Formal Language Theory, Search Techniques, Morphological Processing and Lexical Analysis, Parsing Algorithms for Context-Free Languages, Unification-based Grammars and Parsers, Natural Language Generation, Introduction to Semantic Processing, Ambiguity Resolution Methods
Pre-Requisites	College-level: course on algorithms/programming skills; Minimal exposure to syntax and structure of Natural Language (English)
Co-Requisites	The self-paced Laboratory in NLP (11-712) is designed to complement this course with programming assignments on relevant topics. Students are encouraged to take the lab in parallel with the course or in the following semester.
Course Site
11-712 - Lab in NLP
Description	The Self-Paced Lab in NLP Algorithms is intended to complement the 11-711 lecture course by providing a chance for hands-on, in-depth exploration of various NLP paradigms. Students will study a set of on-line course materials and complete a set of programming assignments illustrating the concepts taught in the lecture course. Timing of individual assignments is left up to the student, although all assignments must be successfully completed and turned in before the end of the semester for the student to receive credit for the course.
Co-Requisites	11-711 - Algorithms for Natural Language Processing
11-713 - Advanced NLP Seminar
Description	This course aims to improve participants' knowledge of current techniques, challenges, directions, and developments in all areas of NLP (i.e., across applications, symbolic formalisms, and approaches to the use of data and knowledge); to hone students' critical technical reading skills, oral presentation skills, and written communication skills; to generate discussion among students across research groups to inspire new research. In a typical semester, a set of readings will be selected (with student input) primarily from the past 2-3 years' conference proceedings (ACL and regional variants, EMNLP, and COLING), journals (CL, JNLE), and relevant collections and advanced texts. Earlier papers may be assigned as background reading. In 2010, the readings will primarily be recent dissertations in NLP. The format of each meeting will include a forty-minute, informal, critical student presentation on the week's readings, with presentations rotating among participants, followed by general discussion. Apart from the presentation and classroom participation, each student will individually write a 3-4-page white paper outlining a research proposal for new work extending research discussed in class - this is similar to the Advanced IR Seminar.
Course Site
11-714 - Tools for NLP
Description	This course is designed as a hands-on lab to help students interested in NLP build their own compendium of the open-source tools and resources available online. Ideally taken in the first semester, the course focuses on one basic topic every two weeks, during which each student will download, install, and play with two or three packages, tools, or resources, and compare notes. The end-of-semester assignment will be to compose some of the tools into a system that does something interesting. We will cover a range, from the most basic tools for sentence splitting and punctuation removal through resources such as WordNet and the Penn Treebank to parsing and Information Extraction engines.
11-716 - Graduate Seminar on Dialog Processing
Description	Dialog systems and processes are becoming an increasingly vital area of interest both in research and in practical applications. The purpose of this course will be to examine, in a structured way, the literature in this area as well as learn about ongoing work. The course will cover traditional approaches to the problem, as exemplified by the work of Grosz and Sidner, as well as more recent work in dialog, discourse and evaluation, including statistical approaches to problems in the field. We will select several papers on a particular topic to read each week. While everyone will do all readings, a presenter will be assigned to overview the paper and lead the discussion. On occasion, a researcher may be invited to present their own work in detail and discuss it with the group. A student or researcher taking part in the seminar will come away with a solid knowledge of classic work on dialog, as well as familiarity with ongoing trends.
11-717 - Language Technologies for Computer Assisted Language Learning
Description	This course studies the design and implementation of CALL systems that use Language Technologies such as Speech Synthesis and Recognition, Machine Translation, and Information Retrieval. After a short history of CALL/LT, students will learn where language technologies (LT) can be used to aid in language learning. From there, the course will explore the specifics of designing software that must interface with a language technology, For each LT, we will explore: • what information does the LT require, • what type of output does the LT send to the CALL interface, • what are the limits of the LT that the CALL designer must deal with, • what are the real time constraints, • what type of training does the LT require The goal of the course is to familiarize the student with : • existing systems that use LT • assessment of CALL/LT software • the limitations imposed by the LT • designing CALL/LT software Grading criteria: • several short quizzes • term project: production of a small CALL/LT system, verbal presentation and written documentation of design of the software.
11-718 - Conversational Interfaces
Description	Conversational Interfaces is intended to bring together an interdisciplinary mix of students from the language technologies institute and the human computer interaction institute to explore the topic of conversational interfaces from a user centered, human impact perspective rather than a heavily technology centered one. In this course we will explore through readings and project work such questions as (1) What are the costs and benefits to using a speech/language interface? (2) When is it advantageous to use a speech/language interface over an alternative? (3) What are the factors involved in the design of effective speech/language interfaces, and what impact do they have on the user's experience with the system? (4) How do we evaluate the usability of a speech/language interface? (5) What have we learned from evaluations of speech/language interfaces that have already been built? To what extent does the data support the claims that are made about the special merits of conversational interfaces?
11-719 - Computational Models of Discourse Analysis
Description	Discourse analysis is the area of linguistics that focuses on the structure of language above the clause level. It is interesting both in the complexity of structures that operate at that level and in the insights it offers about how personality, relationships, and community identification are revealed through patterns of language use. A resurgence of interest in topics related to modeling language at the discourse level is in evidence at recent language technologies conferences. This course is designed to help students get up to speed with foundational linguistic work in the area of discourse analysis, and to use these concepts to challenge the state-of-the-art in language technologies for problems that have a strong connection with those concepts, such as dialogue act tagging, sentiment analysis, and bias detection. This is meant to be a hands on and intensely interactive course with a heavy programming component. The course is structured around 3 week units, all but the first of which have a substantial programming assignment structured as a competition (although grades will not be assigned based on ranking within the competition, rather grades will be assigned based on demonstrated comprehension of course materials and methodology).
Course Site	http://www.cs.cmu.edu/%7Ecprose/discourse-course.html
11-721 - Grammars and Lexicons
Description	Grammars and Lexicons is an introductory graduate course on linguistic data analysis and theory, focusing on methodologies that are suitable for computational implementations. The course covers major syntactic and morphological phenomena in a variety of languages. The emphasis will be on examining both the diversity of linguistic structures and the constraints on variation across languages. Students will be expected to develop and defend analyses of data, capturing linguistic generalizations and making correct predictions within and across languages. The goal is for students to become familiar with the range of phenomena that occur in human languages so that they can generalize the insights into the design of computational systems. The theoretical framework for syntactic and lexical analysis will be Lexical Functional Grammar. Grades will be based on problem sets and take-home exams.
Pre-Requisites	Introductory linguistics course or permission of instructor
11-722 - Grammar Formalisms
Description	The goal of this course is to familiarize students with grammar formalisms that are commonly used for research in computational lingusitics, language technologies, and lingusitics. We hope to have students from a variety disciplines (linguistics, computer science, psychology, modern languages, philosophy) in order to cover a broad perspective in class discussions. Comparison of formalisms will lead to a deeper understanding of human language and natural language processing algorithms. The formalisms will include: Head Driven Phrase Structure Grammar, Lexical Functional Grammar, Tree Adjoining Grammar and Categorial Grammar. If time permits, we will cover Penn Treebank, dependency grammar, and Construction Grammar. We will cover the treatment of basic syntactic and semantic phenomena in each formalism, and will also discuss algorithms for parsing and generating sentences for each formalism. If time permits, we may discuss formal language theory and generative capacity. The course is taught jointly by the following faculty of the Language Technologies Institute: Alan Black Alon Lavie Lori Levin (main coordinator)
11-624 & 11-724 - Human Language for Artificial Intelligence
Description	An enduring aspect of the quest to build intelligent machines is the challenge of human language. This course introduces students with a background in computer science and a research interest in artificial intelligence fields to the structure of natural language, from sound to society. It covers phonetics (the physical aspects of speech), phonology (the sound-structure of language), morphology (the structure of words), morphosyntax (the use of word and phrase structure to encode meaning), syntactic formalisms (using finite sets of production rules to characterize infinite configurations of structure), discourse analysis and pragmatics (language in discourse and communicative context), and sociolinguistics (language in social context and social meaning). Evaluation is based on seven homework assignments, a midterm examination, and a final examination.
11-725 - Meaning in Language
Description	This course provides a survey of the many different ways in which meaning is conveyed in spoken languages, and of the different types of meaning which are conveyed. We will introduce various theoretical frameworks for the description of these phenomena. Topics to be covered will include: word meaning (lexical semantics); structure and meaning (compositional semantics); information structure (foregrounding and backgrounding); verb argument structure and thematic roles; intonational meaning and focus; presupposition; context dependency; discourse markers and utterance modifiers; and the role of inference in interpretation. The topics to be addressed bring together a variety of fields: linguistics; philosophy of language; communication studies and rhetoric; and language technologies. The course may be taken as either a 9-unit (80-306) or 12-unit (80-606/11-725) course. The 12-unit course will include an additional component, which will relate the content of the course to issues in computational linguistics, with an emphasis on methods of implementation. (The computational component will be taught by faculty from the Language Technologies Institute.)
11-726 - Meaning in Language Lab (Self-Paced)
Description	The self-paced Meaning in Language Lab is intended to follow-up on the 11-725 lecture course (Meaning in Language) by providing a chance for hands-on, in-depth, computational exploration of various semantics and pragmatics research topics. The course is self-paced and there will be no scheduled lecture times, however, students are welcome to set up meetings with the instructor as desired, and students who prefer to have a weekly or bi-monthly regularly scheduled meeting with the instructor are welcome to arrange for that. If there is sufficient interest, an informal reading group may be formed to supplement the lab work. Students will design their own project, which they will discuss with the instructor for approval. Students are encouraged to select a topic from semantics, pragmatics, or discourse analysis, such as entailment, evidentiality, implicature, information status, or rhetorical structure, and a topic from language technologies, such as sentiment analysis or summarization, and explore how the linguistic topic applies to some aspect of the chosen language technology. Students are encouraged to contrast symbolic, formal, and knowledge based approaches with empirical approaches. Each student will work independently. If multiple students work as a team on a particular topic, each should choose an approach that is different from the approaches used by the other students working on the same problem. Students will be responsible to set up a web page, blog, or wiki to post progress reports and other supporting documents, data, and analyses. The web space will be checked by the instructor periodically , and thus should be kept updated in order to reflect on-going progress. The web space will also serve as a shared project space in the case that students are working in a team for the project.
11-727 - Computational Semantics for NLP
Description	This course surveys semantics from a language processing perspective. It is divided into three main sections supplemented with a substantive semester-long computational project. The first section addresses traditional topics of computational semantics and semantic processing and representation systems. The second focuses on computational lexical semantics, including resources such as WordNet, Framenet, and some word-based ontologies, and their computational applications, such as word sense disambiguation, entailment, etc., and briefly the semantic web. The third section covers modern statistics-based distributional models of semantics. Each week focuses on one topic, covered by the lecturers, and will include one or two core introductory readings plus several optional more advanced readings. All students will read and discuss the introductory readings while each student will be expected to read advanced papers on at least two topics.
11-731 - Machine Translation
Description	Machine Translation is an introductory graduate-level course surveying the primary approaches and methods for developing modern state-of-the-art automated language translation systems. The main objectives of the course are: Obtain a basic understanding of modern MT systems and MT-related issues. Learn about theory and approaches in Machine Translation and implement the main components of statistical MT systems.
Pre-Requisites	11-711 - "Algorithms for NLP" or equivalent background is recommended.
11-732 - Self-Paced Lab: MT
Description	The Self-Paced Lab in MT is intended to complement the 11-731 lecture course by providing a chance for hands-on, in-depth exploration of various MT paradigms. MT faculty will present a set of possible topics to the students enrolled in the course. The students will indicate their first and second choices for lab projects, and will then be matched to a lab project advisor. At the end of the semester, the students will present the results of their projects in class, and submit a short paper describing them.
Pre-Requisites	11-731 - Machine Translation
11-733 - Multilingual Speech-to-Speech Translation Lab
Description	Building speech-to-speech translation systems (S-2-S) is an extremely complex task, involving research in Automatic Speech Recognition (ASR), Machine Translation (MT), Natural Language Understanding (NLU), as well as Text-to-Speech (TTS) and doing this for many languages doesn't make it easier. Although substantial progress has been made in each of these areas over the last years, the integration of the invididual ASR, MT, NLU, and TTS components to build a good S-2-S system is still a very challenging task. The seminar course on Multilingual Speech-to-Speech Translation will cover important recent work in the areas of ASR, MT, NLU, and TTS with a special focus on language portable approaches and discuss solutions for rapidly building state-of-the-art speech-to-speech translation systems. In the beginning sessions the instructors and other invited lecturers will give a brief introduction into the broad field. We will select papers on particular topics to read by each week. While everyone will do all readings and participate in the discussions, one person is assigned per session to present the basic ideas of the topic specific papers and lead the concluding discussion.
11-734 - Advanced Machine Translation Seminar
Description	The Advanced Machine Translation Seminar is a graduate-level seminar on current research topics in Machine Translation. The seminar will cover recent research on different approaches to Machine Translation (Statistical MT, Example-based MT, Interlingua and rule-based approaches, hybrid approaches, etc.). Related problems that are common to many of the various approaches will also be discussed, including the acquisition and construction of language resources for MT (translation lexicons, language models, etc.), methods for building large sentence-aligned bilingual corpora, automatic word alignment of sentence-parallel data, etc. The material covered will be mostly drawn from recent conference and journal publications on the topics of interest and will vary from year to year. The course will be run in a seminar format, where the students prepare presentations of selected research papers and lead in class discussion about the presented papers.
Pre-Requisites	11-731 - Machine Translation, or instructor approval.
11-736 - Graduate Seminar on Endangered Languages
Description	The purpose of this seminar is to allow students to better understand the linguistic, social and political issues when working with language technologies for endangered languages. Often in LTI we concentrate on issues of modeling with small amounts of data, or designing optimal strategies for collecting data, but ignore many of wider practical issues that appear when working with endangered languages. This seminar will consist of reading books and papers, and having participants give presentations; a few invited talks (e.g. from field linguists, and language advocates) will also be included. It will count for 6 units of LTI course credit. It may be possible for interested students to also carry out a related 6-unit project as a lab.
Course Site	http://www.cs.cmu.edu/%7Eref/sel/
11-741 - Machine Learning with Graphs
Description	Graphs offer a natural way to represent complex relationships among objects of all kinds. Neural network learning with graphs has become important in both academic research and industrial applications. This course (for graduate and undergraduate students who meet the prerequisites) offers a mixture of fundamental concepts, algorithms, basic and advanced models, and broad applications, ranging from social popularity analysis and knowledge graph reasoning to deep learning for solving NP-complete problems.
Pre-Requisites	CS courses on data structures, algorithms and programming (e.g. 15-213), linear algebra (e.g. 21-241 or 21-341) and intro probability (e.g. 21-325) Intro Machine Learning (e.g., 10-701 or 10-601) and Algorithm Design and Analysis (e.g., 15-451) are not required but helpful
Course Site
11-742 - Search Engines
Description	This course studies the theory, design, and implementation of text-based search engines. The core components include statistical characteristics of text, representation of information needs and documents, several important retrieval models, and experimental evaluation. The course also covers common elements of commercial search engines, for example, integration of diverse search engines into a single search service ("federated search", "vertical search"), personalized search results, and diverse search results. The software architecture components include design and implementation of large-scale, distributed search engines. This is a full-semester lecture-oriented course worth 12 units.
Eligibility	This course is open to all students who meet the pre-requisites.
Pre-Requisites	This course requires good programming skills and an understanding of computer architectures and operating systems (e.g., memory vs. disk trade-offs). A basic understanding of probability, statistics, and linear algebra is helpful. Thus students should have preparation comparable to the following CMU undergraduate courses. 15-210, Parallel and Sequential Data Structures and Algorithms (required) 15-213, Introduction to Computer Systems (required) 15-451, Algorithm Design and Analysis (helpful) 21-241, Matrices and Linear Transformations or 21-341, Linear Algebra (required) 21-325, Probability (helpful) 36-202, Methods for Statistics & Data Science (helpful)
Website	http://boston.lti.cs.cmu.edu/classes/11-742/
11-743 - Self-Paced Lab: IR
Description	Advanced Information Retrieval Seminar and Lab is a seminar that focuses on current research in Information Retrieval. The seminar covers recent research on subjects such as retrieval models, text classification, information gathering, fact extraction, information visualization, summarization, text data mining, information filtering, collaborative filtering, question answering systems, and portable information systems. Other topics are drawn from recent SIGIR, Digital Libraries, TREC, Machine Learning, and AAAI conferences. Course content varies from year to year. Students not taking the course for credit are welcome to audit or sit in on the course, subject to availability of space.
Pre-Requisite	11-742, Search Engines (required)
Course Site	http://nyc.lti.cs.cmu.edu/classes/11-743 (accessible on campus, or through VPN for those who are off campus).
11-744 - Experimental Information Retrieval
Description	This seminar studies the experimental evaluation of information retrieval systems in community-wide evaluation forums such as TREC, CLEF, NTCIR, INEX, TAC, and other annual research evaluations. The content will change from year to year, but the general format will be an in-depth introduction to the evaluation forum; its tracks or tasks, test collections, evaluation methodologies, and metrics; and several of the most competitive or interesting systems in each track or task. Class discussions will explore and develop new methods that might be expected to be competitive. The seminar includes a significant project component in which small teams develop systems intended to be competitive with the best recent systems. Students are not required to participate in actual TREC, CLEF, etc., evaluations, however some students may wish to do so. A specific goal of the seminar is to prepare students to compete effectively in such evaluations. The course meets twice a week during the first half of the semester. This part of the course is a combination of seminar-style presentations and brainstorming sessions about how to build competitive systems. The course meets once a week during the second half of the semester, when students are doing their projects. This part of the class is essentially weekly progress reports about student projects.
Pre-Requisites	11-642 - Search Engines
Course Site	http://boston.lti.cs.cmu.edu/classes/11-744/
11-751 - Speech Recognition and Understanding
Description	The technology to allow humans to communicate by speech with machines or by which machines can understand when humans communicate with each other is rapidly maturing. This course provides an introduction to the theoretical tools as well as the experimental practice that has made the field what it is today. We will cover theoretical foundations, essential algorithms, major approaches, experimental strategies and current state-of-the-art systems and will introduce the participants to ongoing work in representation, algorithms and interface design. This course is suitable for graduate students with some background in computer science and electrical engineering, as well as for advanced undergraduates. Prerequisites: Sound mathematical background, knowledge of basic statistics, good computing skills. No prior experience with speech recognition is necessary. This course is primarily for graduate students in LTI, CS, Robotics, ECE, Psychology, or Computational Linguistics. Others by prior permission of instructor.
Pre-Requisites	Sound mathematical background, knowledge of basic statistics, good computing skills. No prior experience with speech recognition is necessary. This course is primarily for graduate students in LTI, CS, Robotics, ECE, Psychology, or Computational Linguistics. Others by prior permission of instructor.
Course Site
11-752 - Speech Generation
Description	This course provides an in-depth study of speech synthesis and generative speech modeling, tracing the evolution from classical signal-processing and statistical approaches to modern neural and foundation-based methods. The course begins with the fundamentals of human speech production, which motivate early synthesis techniques such as concatenative and statistical parametric models, and then progresses to deep learning-based acoustic modeling, neural vocoding, and end-to-end speech generation architectures. Contemporary topics include the integration of large language models with speech synthesis, the use of discrete and continuous speech representations, and emerging paradigms for scalable and controllable speech generation. Beyond text-to-speech, the course covers a broad range of generative speech problems, including voice conversion, expressive speech synthesis, prosody generation and manipulation, speech editing, speech-to-speech translation, and spoken dialogue systems. Additional topics address disentangled representation learning, evaluation methodologies, speech databases, and security considerations such as spoofing attacks. Days of Week: Monday, Wednesday Time: 9:30 - 10:50 Enrollment is 30 Will you have a final exam? No, final project Letter grades or option to Pass/Fail? Letter grades Best regards, Carlos ________________________________ Professor, IEEE Fellow, ISCA Fellow Carnegie Mellon University School of Computer Science Language Technologies Institute
11-753 - Advanced Laboratory in Speech Recognition
Description	The technology to allow humans to communicate by speech with machines or by which machines can understand when humans communicate with each other is rapidly maturing. While the 11-751 speech course focussed on an introduction to the theoretical foundations, essential algorithms, major approaches, and strategies for current state-of-the-art systems, the 11-753 speech lab complements the education by concentrating on the experimental practice in developing speech recognition and understanding speech-based systems, and by getting hands-on experience on relevant research questions using state-of-the art tools. Possible problem sets include both core speech recognition technology, and the integration of speech-based components into multi-modal, semantic, learning, or otherwise complex systems and interfaces.
Pre-Requisites	11-751 or equivalent; this course can be combined with 11-783 for a 12-unit lab
Course Site
11-754 - Project Course: Conversational Systems
Description	This course will teach participants how to implement a complete spoken language system while providing opportunities to explore research topics of interest in the context of a functioning system. The course will produce a complete implementation of a system to access and manipulate email through voice only, for example to allow users to interact with the mail system over a telephone while away from their computer. In doing so the class will address the component activities of spoken language system building. These include, but are not limited to, task analysis and language design, application-specific acoustic and language modeling, grammar design, task design, dialog management, language generation and synthesis. The course will place particular emphasis on issues in task design and dialog management and on issues in language generation and synthesis. For Fall, we will implement a simple telephone-based information access application. The domain is bus schedules and the goal will be to create one or more usable applications that can provide a real service and can be deployed for actual use by the University community. Participants will chose individual components of the system to concentrate on and will collaborate to put together the entire system. It is perfectly acceptable for several individuals to concentrate on a single component, particularly if their work will exemplify alternative approaches to the same problem.
Pre-Requisites	Speech Recognition or permission of the instructor.
11-755 - Machine Learning for Signal Processing
Description	Signal Processing is the science that deals with extraction of information from signals of various kinds. This has two distinct aspects -- characterization and categorization. Traditionally, signal characterization has been performed with mathematically-driven transforms, while categorization and classification are achieved using statistical tools. Machine learning aims to design algorithms that learn about the state of the world directly from data. A increasingly popular trend has been to develop and apply machine learning techniques to both aspects of signal processing, often blurring the distinction between the two. This course discusses the use of machine learning techniques to process signals. We cover a variety of topics, from data driven approaches for characterization of signals such as audio including speech, images and video, and machine learning methods for a variety of speech and image processing problems.
11-756 - Design and Implementation of Speech Recognition Systems
Description	Voice recognition systems invoke concepts from a variety of fields including speech production, algebra, probability and statistics, information theory, linguistics, and various aspects of computer science. Voice recognition has therefore largely been viewed as an advanced science, typically meant for students and researchers who possess the requisite background and motivation. In this course we take an alternative approach. We present voice recognition systems through the perspective of a novice. Beginning from the very simple problem of matching two strings, we present the algorithms and techniques as a series of intuitive and logical increments, until we arrive at a fully functional continuous speech recognition system. Following the philosophy that the best way to understand a topic is to work on it, the course will be project oriented, combining formal lectures with required hands-on work. Students will be required to work on a series of projects of increasing complexity. Each project will build on the previous project, such that the incremental complexity of projects will be minimal and eminently doable. At the end of the course, merely by completing the series of projects students would have built their own fully-functional speech recognition systems. Grading will be based on project completion and presentation.
Pre-Requisites	Mandatory: Linear Algebra. Basic Probability Theory. Recommended: Signal Processing. Coding Skills: This course will require significant programming from the students. Students must be able to program fluently in at least one language (C, C++, Java, Python, LISP, Matlab are all acceptable).
Course Site	http://www.cs.cmu.edu/afs/cs/user/bhiksha/WWW/courses/11-756.asr/spring2011/
11-761 - Language and Statistics
Description	The goal of "Language and Statistics" is to ground the data-driven techniques used in language technologies in sound statistical methodology. We start by formulating various language technology problems in both an information theoretic framework (the source-channel paradigm) and a Bayesian framework (the Bayes classifier). We then discuss the statistical properties of words, sentences, documents and whole languages, and the various computational formalisms used to represent language. These discussions naturally lead to specific concepts in statistical estimation. Topics include: Zipf's distribution and type-token curves; point estimators, Maximum Likelihood estimation, bias and variance, sparseness, smoothing and clustering; interpolation, shrinkage, and backoff; entropy, cross entropy and mutual information; decision tree models applied to language; latent variable models and the EM algorithm; hidden Markov models; exponential models and the maximum entropy principle; semantic modeling and dimensionality reduction; probabilistic context-free grammars and syntactic language models.
Course Site
11-762 - Language and Statistics II
Description	This course will cover modern empirical methods in natural language processing. It is designed for language technologies students who want to understand statistical methodology in the language domain, and for machine learning students who want to know about current problems and solutions in text processing. Students will, upon completion, understand how statistical modeling and learning can be applied to text, be able to develop and apply new statistical models for problems in their own research, and be able to critically read papers from the major related conferences (EMNLP and .ACL). A recurring theme will be the tradeoffs between computational cost, mathematical elegance, and applicability to real problems. The course will be organized around methods, with concrete tasks introduced throughout. The course is designed for SCS graduate students. This course is taught intermittently. Students interested in this topic may also wish to consider 11-763 - Structured Prediction for Language and Other Discrete Data, which covers similar material.
Pre-Requisites	Mandatory: 11-761 - Language and Statistics, or permission of the instructor. Recommended: 11-711 - Algorithms for Natural Language Processing; 10-601 or 10-701 - Machine Learning; or 11-745 - Advanced Statistical Learning Seminar
Course Site
11-763 - Inference Algorithms for Language Modeling
Description	As the use of massive and costly-to-train large language models has become increasingly commonplace, much academic and industry interest has focused on the methods used to generate outputs from these models - the inference process. Inference-time algorithms can be applied on top of an already-trained model to improve generation quality, lower latency, or induce additional controllability. Inference-time algorithms can allow users to run models on their laptop, serve millions of outputs at scale, or dramatically increase the quality of generations from a system without additional training. In this class, we survey the wide space of inference-time techniques with a particular focus on the implementation and practical use cases of such methods. Students will understand the different ways to implement and compare inference-time techniques, learn the theory behind different strategies for inference-time scaling of compute, and implement representative examples from several classes of inference-time algorithms. In the final project, students will apply inference-time strategies of their choice to two shared tasks: an open-ended generation task and a reasoning task.
Pre-Requisites	10-601 or 10-701 - Machine Learning or instructors' permission.
Course Site
11-765 - Active Learning Seminar
Description	Participants will read and present papers, including analyzing comparative strengths and weaknesses of various algorithms. Meetings will take place once a week for about two hours in the fall.
Pre-Requisites	A graduate-level machine learning course.
11-766 - Large Language Models Applications
Description	Large language models are used today for many more applications than just modelling language. In this course, we learn how Transformer-based neural language models have been adapted for applications ranging from web agents and retrieval systems to music generation and coding and writing assistants. We will systematically examine the ways different applications adapt the base technologies underlying large language models through varying choices in pre-training and fine-tuning data, model architecture, training objective, and usage of inference-time compute. This class is intended for students who have already learned the basics of how language models are implemented and are curious to develop an understanding of the advanced techniques used for customizing them to different applications.
11-768 - AI Agents
Description	This course focuses on LLM-based AI agents: autonomous systems that use large language models to perceive, reason, plan, and act in complex environments. These systems represent a shift toward multi-step task completion requiring sustained interaction with external environments. LLM agents have already demonstrated a substantial impact on software development through code generation, and are poised to impact other digital domains. Developing capable agents requires specializing standard LLM training methods, including supervised fine-tuning and reinforcement learning, to the agentic domains. These systems face unique research challenges such as environmental grounding and long-horizon credit assignment, which provide significant opportunities for technical exploration. This course covers foundational capabilities like instruction following, tool use, memory, and task decomposition alongside practical considerations such as safety sandboxing, credentialing, and the use of modern development frameworks. Students will learn these established foundations as well as explore open research frontiers: advanced search techniques, and interaction with people and other agents. This course is intended for graduate students. Familiarity with training neural language models is required.
11-772 - Analysis of Social Media
Description	The most actively growing part of the web is "social media" (wikis, blogs, bboards, and collaboratively-developed community sites like Flikr and YouTube). This course will review selected papers from recent research literature that address the problem of analyzing and understanding social media. Topics to be covered include: -Text analysis techniques for sentiment analysis, analysis of figurative language, authorship attribution, and inference of demographic information about authors (age or sex). -Community analysis techniques for detecting communities, predicting authority, assessing influence (in viral marketing), or detecting spam. -Visualization techniques for understanding the interactions within and between communities. -Learning techniques for modeling and predicting trends in social media, or predicting other properties of media (user-provided content tags.)
Pre-Requisites	10-601 or 10-701 - Machine Learning or instructors' permission.
11-775 - Large-Scale Multimedia Analysis
Description	Can a robot watch “Youtube" to learn about the world? What makes us laugh? How to bake a cake? Why is Kim Kardashian famous? 12-unit class covering fundamentals of computer vision, audio and speech processing, multi-media files and streaming, multi-modal signal processing, video retrieval, semantics, and text (possibly also: speech, music) generation. Instructors will give an overview of relevant recent work and benchmarking efforts (Trecvid, Mediaeval, etc.). Students will work on research projects to explore these ideas and learn to perform multi-modal retrieval, summarization and inference on large amounts of “Youtube”-style data. The experimental environment for the practical part of the course will be given to students in the form of Virtual Machines.
Pre-Requisites	This is a graduate course primarily for students in LTI, HCII, CSD, Robotics, ECE; others, for example (undergraduate) students of CS or professional masters, by prior permission of the instructor(s). Strong implementation skills, and familiarity with some (not all) of the above fields (e.g. 11-611, 11-711, 11-751, 11-755, 11-792, 16-720, or equivalent), will be helpful.
Course Site
11-776 - Human Communication and Multimodal Machine Learning
Description	Humans are highly social creatures and have evolved complex mechanisms for signaling information about their thoughts, feelings, and intentions (both deliberately and reflexively). In turn, humans have also evolved complex mechanisms for receiving these signals and inferring the thoughts, feelings, and intentions of others. Proper understanding of human behavior, in all its nuance, requires careful consideration and integration of verbal, vocal, and visual information. These communication dynamics have long been studied in psychology and other social sciences. More recently, the field of multimodal affective computing has sought to enhance these studies using techniques from computer science and artificial intelligence. Common topics of study in this field include affective states, cognitive states, personality, psychopathology, social processes, and communication. As such, multimodal affective computing has broad applicability in both scientific and applied settings ranging from medicine and education to robotics and marketing. The objectives of this course are: (1) To give an overview of the components of human behavior (verbal, vocal, and visual) and the computer science areas that measure them (NLP, speech processing, and computer vision) (2) To provide foundational knowledge of psychological constructs commonly studied in multimodal affective computing (e.g., emotion, personality, and psychopathology) (3) To provide practical instruction on using statistical tools to study research hypotheses (4) To provide information about computational predictive models that integrate multimodal information from the verbal, vocal, and visual modalities (5) To give students practical experience in the computational study of human behavior and psychological constructs through an in-depth course project
11-777 - Multimodal Machine Learning
Description	Multimodal machine learning (MMML) is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages. With the initial research on audio-visual speech recognition and more recently with language vision projects such as image and video captioning, this research field brings some unique challenges for multimodal researchers given the heterogeneity of the data and the contingency often found between modalities. The course will present the fundamental mathematical concepts in machine learning and deep learning relevant to the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation & mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. These include, but not limited to, multimodal auto-encoder, deep canonical correlation analysis, multi-kernel learning, attention models and multimodal recurrent neural networks. We will also review recent papers describing state-of-the-art probabilistic models and computational algorithms for MMML and discuss the current and upcoming challenges. The course will discuss many of the recent applications of MMML including multimodal affect recognition, image and video captioning and cross-modal multimedia retrieval. This is a graduate course designed primarily for PhD and research master students at LTI, MLD, CSD, HCII and RI; others, for example (undergraduate) students of CS or from professional master programs, are advised to seek prior permission of the instructor. It is strongly recommended for students to have taken an introduction machine learning course such as 10-401, 10-601, 10-701, 11-663, 11-441, 11-641 or 11-741. Prior knowledge of deep learning is recommended but not required.
11-780 - Research Design and Writing
Description	In an increasingly competitive research community within a rapidly changing world, it is essential that our students formulate research agendas that are of enduring importance, with clean research designs that lead to generalizable knowledge, and with high likelihood of yielding results that will have impact in the world. However, even the best research, if not communicated well, will fail to earn the recognition that it deserves. Even more seriously, the most promising research agendas, if not argued in a convincing and clear manner, will fail to secure the funding that would give them the chance to produce those important results. Thus, in order to complement the strong content-focused curriculum in LTI, we are proposing a professional skills course that targets the research and writing methodology that our students will need to excel in the research community, both during their degree at LTI and in their career beyond. This course focuses specifically on general experimental design methodology and corresponding writing and reporting skills. Grades will be based on a series of substantial writing assignments in which students will apply principles from experimental design methodology, such as writing an IRB application, a research design, a literature review, and a conference paper with data analysis and interpretation. A final exam will test skills and concepts related to experimental design methodology, and will include short answer questions and a critique of a research paper.
11-781 - Generative AI for Biomedicine
Description	"Recent progress of Artificial Intelligence has been transforming the approaches of scientific research across various disciplines. Generative AI models, such as AlphaFold, have become indispensable tools in fundamental biomedical research. This course offers students an opportunity to explore the latest developments in generative AI applied to biomedicine. Topics include models and methods for the prediction of protein structure from sequences, characterization of genome functions and interactions, modeling of cellular structures and tissue organizations, single cell biology, and drug design. We will cover a variety of models, such as pre-trained biological foundation models, diffusion models, Monte Carlo methods, graph neural networks, etc. Through this course, students will gain a deep understanding of how generative AI can be leveraged to address complex challenges in biomedicine. Specifically, we have the following Learning Objectives: 1. Solid understanding generative AI models. 2. Comprehensive knowledge and indisciplinary thinking about generative AI application to key biomedical applications, e.g., protein structure, regulatory sequence design, cellular structure and function, and drug design 3. Critical analysis of research papers on generative AI methodologies and their applications to biomedicine. 4. Project-based learning and problem solving. "
11-782 - Self-Paced Lab for Computational Biology
Description	Students will choose from a set of projects designed by the instructor. Students will also have the option of designing their own projects, subject to instructor approval. For the students who had completed a project in the 10-810 course, they can either switch to another project, or continue working on the previous project by aiming a significant progress (subject to instructor approval). Each student will work independently. If more than one student work on a particular topic, each should choose an approach that is different from the approaches used by the other students working on the same problem. The students need to begin with a project proposal to outline the high-level ideas, tasks, and goals of the problem, and plan of experiments and/or analysis. The instructor will consult with you on your ideas , but the final responsibility to define and execute an interesting piece of work is yours. Your project will have two final deliverables: 1. a writeup in the form of a NIPS paper (8 pages maximum in NIPS format, including references), worth 60% of the project grade, and 2. a research seminar presentation of your work at the end of the semester, worth 20% of the project grade. In addition, you must turn in a midway progress report (5 pages maximum in NIPS format, including references) describing the results of your first experiments, worth 20% of the project grade. Note that, as with any conference, the page limits are strict! Papers over the limit will not be considered. The grading of your project are based on overall scientific quality, novelty, writing, and clarity of presentation. We expect your final report to be of conference-paper quality, and you are expected to also deliver software implementation of your algorithmic results.
Pre-Requisites	10-810 - Advanced Algorithms and Model for Computational Biology
Co-Requisites	10-810 - Advanced Algorithms and Model for Computational Biology
11-783 - Self-Paced Lab: Rich Interaction in Virtual World
Description	Massively Multi-player Online Role-Playing Games have evolved into Virtual Worlds (VWs), and are creating ever richer environments for experimentation on all aspects of human to human, or human to machine communication, as well as for information discovery and access. So far, interaction has been constrained by the limited capabilities of keyboards, joysticks, or computer mice. This creates an exciting opportunity for explorative research on speech input and output, speech-to-speech translation, or any aspect of language technology. Of particular interest will be a combination with other novel "real world" (RW) input, or output devices, such as mobile phones or portable games consoles, because they can be used to control the VW, or make it accessible everywhere in RW. Language technologies in particular profit from "context awareness", because domain adaptation can be performed. For scientific experimentation in that area, Virtual Worlds offer the opportunity to concentrate on algorithms, because context sensors can be written with a few lines of code, without the need for extra hardware sensors. Algorithms can also run "continuously", without the need for specific data collection times or places, because the VW is "always on". In this lab, we will enhance existing clients to virtual worlds so that they can connect to various speech and language related research systems developed at LTI and CMU's Silicon Valley campus. The lab will be held jointly at the CMU's Pittsburgh and Silicon Valley Campuses. We will "eat our own dog food", so the goal will be to hold the last session entirely in a virtual class room, which will by that time include speech control of virtual equipment, speech-to-speech translation, and some devices that can be controlled using non-PC type equipment, like mobile phones.
Pre-Requisites	11-751 or equivalent; this course can be combined with 11-753 for a 12-unit lab
Course Site
11-785 - Introduction to Deep Learning
Description	Neural networks have increasingly taken over various AI tasks, and currently produce the state of the art in many AI tasks ranging from computer vision and planning for self-driving cars to playing computer games. Basic knowledge of NNs, known currently in the popular literature as deep learning, familiarity with various formalisms, and knowledge of tools, is now an essential requirement for any researcher or developer in most AI and NLP fields. This course is a broad introduction to the field of neural networks and their deep learning formalisms. The course traces some of the development of neural network theory and design through time, leading quickly to a discussion of various network formalisms, including simple feedforward, convolutional, recurrent, and probabilistic formalisms, the rationale behind their development, and challenges behind learning such networks and various proposed solutions. We subsequently cover various extensions and models that enable their application to various tasks such as computer vision, speech recognition, machine translation and playing games. Instruction Unlike prior editions of 11-785, the instruction will primarily be through instructor lectures, and the occasional guest lecture. Evaluation Students will be evaluated based on weekly continuous-evaluation tests, and their performance in assignments and a final course project. There will be six hands-on assignments, requiring both low-level coding and toolkit-based implementation of neural networks, covering basic MLP, convolutional and recurrent formalisms, as well as one or more advanced tasks, in addition to the final project.
11-787 - AI Cofounder: A Startup Builder's Guide
Description	AI Cofounder: A Startup Builder's Guide is a 13-week, project-based course for students who want to launch AI-centered startups and lead as the technical or AI cofounder. The course offers a practical framework for ideating, building, leading, and scaling a technology venture—from idea to market validation and fundraising prep—while adapting in real time to evolving AI tools and trends. Through applied workshops, founder stories, and domain-specific case studies (fintech, health-tech, climate, and more), students will develop their own founder's playbook, translating technical expertise into entrepreneurial action. By the end of the course, each participant will have crafted a validated startup concept with a financial plan, go-to-market strategy, and pitch, culminating in a public poster session during AIVS.co's demo day.
Course Site	https://cs.cmu.edu/~11787-AI-Cofounder
11-791 - Software Engineering for Information Systems
Description	The Software Engineering for IT sequence combines classroom material and assignments in the fundamentals of software engineering (11-791) with a self-paced, faculty-supervised directed project (11-792). The two courses cover all elements of project design, implementation, evaluation, and documentation. For students intending to complete both courses, it is recommended that the project design and proof-of-concept prototype be completed and approved by the faculty advisor before the start of 11-792, if possible. Students may elect to take only 11-791; however, if both parts are taken, they should be taken in proper sequence.
Course Site
11-792 - Intelligent Information Systems Project
Description	The Software Engineering for IS sequence combines classroom material and assignments in the fundamentals of software engineering (11-791) with a self-paced, faculty-supervised directed project (11-792). The two courses cover all elements of project design, implementation, evaluation, and documentation. Students may elect to take only 11-791; however, if both parts are taken, they should be taken in proper sequence. Prerequisite: 11-791. The course is required for VLIS students.
Pre-Requisites	11-791 - Software Engineering for Information Systems
Course Site
11-794 - Inventing Future Services
Description	Inventing the Future of Services is a course that focuses on the development of innovative thinking in a business environment. CMU graduates should not be waiting for their employers to tell them what to do – they should be driving radical innovation in their businesses. Drawing on 17 years experience directing applied research at Accenture Technology Labs, the instructor teaches students systematic approaches to technology-driven business innovation in services industries.
Course Site	http://www.cs.cmu.edu/~anatoleg/Inventing%20the%20Future%20of%20Services%20Course%20descr%20Fall%202011.htm
11-795 - Seminar: Algorithms for Privacy and Security
Description	Alice wants an answer from Bob. But she does not want Bob to know the question! Charlie puts up pictures on the web. Bob downloads one of them from Flickr. How can he be sure the picture was Charlie's and not a counterfeit from Mallory? A secret must be distributed among N people so that a minimum of T of them must pool their knowledge in order to learn anything about the recipe? Answers to questions such as the above (many lie in a variety of computational fields such as Cryptography, Secure Multi-Party Computation, Watermarking, Secret Sharing. In this course we will cover a variety of topics related to privacy and security, including basic cryptography, secret sharing, privacy-preserving computation, data-hiding and steganography, and the latest algorithms for data mining with privacy. This will be a participatory course. Students will be required to present 1-3 papers during the semester. Papers must be analysed and presented in detail. Discussion and questions will be encouraged. Grading will be based on participation and presentation.
Pre-Requisites	Recommended: Abstract Algebra, Number Theory.
Course Site	http://www.cs.cmu.edu/afs/cs/user/bhiksha/WWW/courses/11-795.privacy/
11-796 - Question Answering Lab
Description	The Question Answering Lab course provides a chance for hands-on, in-depth exploration of core algorithmic approaches to question answering (QA). Students will work independently or in small teams to extend or adapt existing QA modules and systems to improve overall performance on known QA datasets (e.g. TREC, CLEF, NTCIR, Jeopardy!), using best practices associated with the Open Advancement of Question Answering initiative. Projects will utilize existing components and systems from LTI (JAVELIN, Ephyra) and other open source projects (UIMA-AS, OAQA) running on a 10-node distributed computing cluster. Each student project will evaluate one or more component algorithms on a given QA dataset and produce a conference-style paper describing the experimental setup and results. Format: The course will require weekly in-class progress meetings with the instructors, in addition to individual self-paced work outside the classroom.
Pre-Requisites	Intermediate Java programming skills.
11-797 - Question Answering
Description	This course is primarily a project course. During the first few weeks of the course, students complete a set of readings (which are complemented by instructor presentations) and associated reading quizzes. Then the entire class will brainstorm ideas for new approaches to a current data set or leaderboard. After formulating specific hypotheses about how to extend the state of the art, the class will split into teams in order to implement and evaluate the associated experimental conditions for each hypothesis. The overall goal is to extend the state of the art performance on the given data set or leaderboard, and possibly submit a technical publication which describes the work in detail (when the results demonstrate a statistically significant improvement versus the state of the art). During each remaining week in the semester, student teams will present their incremental project progress, followed by group discussion. During Weeks 13-15, each student team will make a final presentation. A final report / paper will be due during Week 16. The final grade will be based on all the deliverables (project proposal, final presentation, final report / paper).
Pre-Requisites	Intermediate Java programming skills.
11-801 - Quantitative Evaluation of Language Technologies
Description	Evaluating NLP models for properties like quality, fluency, safety, or revenue generation is a fundamental part of model development in research and engineering contexts. This course will present fundamental principles of evaluation, focusing on both offline contexts such as benchmark datasets and online environments such as production systems. Material will be organized into two parts. The first part will focus on measurement of system decisions, covering the principles of metric design (i.e., what makes a metric appropriate for a given NLP task), data elicitation (i.e., how can we gather data associated with what we are trying to measure), and modeling system properties (i.e., how can we formally model the properties like quality). The second part will focus on comparing systems given a set of measured values. We will cover dataset construction and hypothesis testing in both offline and online contexts. The course will include lectures from external researchers and practitioners using evaluation techniques to audit systems, design new benchmarks, and deploy production models.
11-811 - Interdisciplinary NLP: Language Modeling in the Wild
Description	Recent advances in natural language processing (NLP), primarily powered by large language models (LLMs) show great potential for enabling advanced analysis of unstructured and semi-structured documents across a diverse array of applications — from accelerating scientific discovery by automatically analyzing materials science research literature, to facilitating a study of the evolution of narrative arcs in 20th century literature. Historically, successful real world deployment has often required deliberate adaptation: careful definition of the task, curation of new or existing datasets, experimentation to identify strengths and limitations of existing off-the-shelf affordances, and/or consideration of computational and financial feasibility. On the other hand, recent developments in language technologies have included both 1) meaningful capability improvements in many settings that until recently were outside the scope of existing tools, and 2) lowered barriers to use and adaptation of language technologies. In this class, students with concentrations outside of NLP (e.g. degree programs in materials science, English, ...) and students with concentrations in or near NLP (LTI, MLD or equivalent expertise) will work with and learn from each other, to characterize and bridge gaps between the promise of modern language technologies and the successful deployment of these tools for real-world applications. Together, students will explore: Technical foundations for using language technologies, AI literacy and effective science communication; Identifying strengths and limitations of various approaches for adaptation to a specific domain or setting, and; Acquiring and curating data appropriate to a specific task or evaluation; Devising and executing a plan to accomplish research and analysis tasks given a goal
Course Site	https://clarasna.com/teaching/11-811/
11-823 - ConLanging: Learning About Linguistics and Language Technologies Through Construction of Artificial Languages
Description	Students will work individually or in small groups to create artificial human(oid) languages for fictional human cultures or SciFi worlds. Students will implement language technologies for their languages. In the course of creating the languages, students will learn about the building blocks of human language such as phones, phonemes, morphemes, and morpho-syntactic constructions including their semantics and pragmatics. Class instruction will focus specifically on variation among human languages so that the students can make conlangs that are not just naively English-like. We will also touch on philosophical issues in philosophy of language and on real-world socio-political issues related to language policy. Students will be required to use at least one of the following technologies: language documentation tools that are used for field linguistics and corpus annotation, automatic speech recognition, speech synthesis, morphological analysis, parsing, or machine translation. Learning Objectives: 1. The building blocks (phonemes, morphemes, etc.) of language, how languages are built from them, and how they interact 2. Metalinguistic awareness and knowledge about variation in human language 3. Language, thought, and culture: how does language reflect thought and culture, and vice versa. Why wouldn't Elvish be a good language for Klingons? 4. Language policy in the real world: For students who want to manipulate real languages. 5. Historical linguistics and language change: for students who want to manipulate real languages or make families of related conlangs for fictional worlds. 6. Practical experience with a language technology. http://tts.speech.cs.cmu.edu/11-823/
Course Site
11-824 - Subword Modeling
Description	The goal of this course is to lead students to engage broadly with the existing NLP and computational linguistics research on subword modeling and develop new computational approaches to problems in morphology, orthography, and phonology. In addition to three other miniprojects, students will be expected to produce one piece of research that can be developed into a conference or workshop paper (though submission is not a course requirement). The paper should be suitable for the Phonology, Morphology, and Word Segmentation tracks of the *ACL conferences, the SIGMORPHON workshop, Coling, or LREC.
Pre-Requisites	11-411 or 11-611 or 11-711
11-830 - Ethics, Safety, and Social Impact in NLP and LLMs
Description	As language technologies have become increasingly prevalent, there is a growing awareness that decisions we make about our data, methods, and tools are often tied up with their impact on people and societies. This course introduces students to real-world applications of language technologies and the potential ethical implications associated with them. We discuss philosophical foundations of ethical research along with advanced state-of-the art techniques. Discussion topics include: - Philosophical foundations: ethical philosophies, history, medical and psychological experiments, IRB and human subjects, ethical decision making, AI alignment. - Bias, Misrepresentation, Alignment: algorithms to identify biases in models and data and adversarial approaches to debiasing. - Civility in communication: techniques to monitor trolling, hate speech, abusive language, cyberbullying, toxic comments. - Democracy and the language of manipulation: approaches to identify propaganda and manipulation in news, to identify fake news, political framing. - Privacy & security : algorithms for demographic inference, personality profiling, and anonymization of demographic and personal traits. - NLP for Social Good: Low-resource NLP, applications for disaster response and monitoring diseases, medical applications, psychological counseling, interfaces for accessibility. - Multidisciplinary perspective: invited lectures from experts in behavioral and social sciences, rhetoric, etc.
Course Site	https://maartensap.com/11830/
11-860 - Quantum Computing Cryptography and Machine Learning Lab
Description	Students will gain familiarity with current universal gate quantum computing tools and technology. Students will also become comfortable with several QC algorithms and their implementation on state of the art quantum computer simulators and hardware. (Taught TR - time to be determined - please see course web page for additional information)
Course Site	https://thequantumturtle.github.io/QuantumSpring2022/
11-866 - Artificial Social Intelligence
Description	This course is designed to be a graduate-level seminar course on artificial social intelligence, a vibrant multi-disciplinary research field with the aim of building AI that can perceive human social cues, intents, and psychological states, engage in social interaction, and understand social norms and commonsense. The course will focus on reading and dissecting research papers in this field, spanning foundational topics in multimodal artificial intelligence, human-computer interaction, social commonsense reasoning, theories of social intelligence, and ethical implications of artificial social intelligence, alongside real-world applications in social robotics, affective computing, conversational agents, healthcare, education, and other domains. Students are expected to have prior experience involving basic artificial intelligence and have a deep interest in social intelligence.
Course Site	https://cmu-multicomp-lab.github.io/cmu-asi-course/spring2026/
11-868 - Large Language Model Systems
Description	Recent progress of Artificial Intelligence has been largely driven by advances in large language models (LLMs) and other generative methods. These models are often very large (e.g. 175 billion parameters for GPT3) and requires increasingly larger data to train (e.g. 300 billion tokens for ChatGPT). Training, serving, fine-tuning, and evaluating LLMs require sophisticated engineering with modern hardware and software stacks. Developing scalable systems for large language models is critical to advance AI. In this course, students will learn the essential skills to design and implement LLM systems. This includes algorithms and system techniques to efficiently train LLMs with huge data, efficient embedding storage and retrieval, data efficient fine-tuning, communication efficient algorithms, efficient implementation of reinforcement learning with human feedback, acceleration on GPU and other hardware, model compression for deployment, and online maintenance. We will cover the latest advances about LLM systems in machine learning, natural language processing, and system research.
Course Site	https://llmsystem.github.io/
11-884 - AI & Emerging Economies
Description	The unique course design offers students the opportunity to work in global project teams with CMU students from around the world, esp. Africa to gain a better understanding of different perspectives and approaches towards AI. AI and contemporary tools like ChatGPT and agents, experiential learning, data, and a diverse set of case studies will help inculcate critical and creative thinking as well as novel problem-solving approaches. You will work on practical projects that apply your newfound knowledge to real-world scenarios. By the end of the course, you will have a deep understanding of the opportunities and challenges of AI in emerging economies, as well as the skills and experience necessary to work effectively in global teams. The course is multi- disciplinary and germane to budding technologists, entrepreneurs, and policy influencers. This is an opportunity not to be missed!
Course Site	https://www.heinz.cmu.edu/current-students/courses/94-894
11-899 - Summarization and Personal Information Management
Description	The problem of information overload in personal communication media such as email, instant messaging, and on-line forums is a well documented phenomenon. Much work addressing this problem has been conducted separately in the human-computer interaction (HCI) community, the information sciences community, and the computational linguistics community. However, in each case, while important advancements in scientific knowledge have been achieved, the work suffers from an "elephant complex", where each community focuses mainly on just the part of the problem most visible from their own perspective. The purpose of this course is to bring these threads together to examine the issue of managing personal communication data from an integrated perspective.
11-904 - Python for Data Science I
Description	Students learn the concepts, techniques, skills, and tools needed for developing programs in Python. Core topics include types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations. Students get an introductory experience with several development environments, including Jupyter Notebook, as well as selected software development practices, such as test-driven development, debugging, and style. Course projects include real-life applications on enterprise data and document manipulation, web scraping, and data analysis.
11-905 - Python for Data Science II
Description	Students learn the concepts, techniques, skills, and tools needed for developing programs in Python. Core topics include types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations. Students get an introductory experience with several development environments, including Jupyter Notebook, as well as selected software development practices, such as test-driven development, debugging, and style. Course projects include real-life applications on enterprise data and document manipulation, web scraping, and data analysis.
11-910 - Directed Research
Description	This course number documents the research being done by Masters and pre-proposal PhD students. Beginning in Fall 2001, every LTI graduate student will register for at least 24 units of 11-910 each semester, unless they are ABD (i.e., they have had a thesis proposal accepted), in which case they should register for 48 units of 11-930. The student will be expected to write a report and give a presentation at the end of the semester, documenting the research done. The report will be filed by either the faculty member or the LTI graduate program administrator. (Until Fall 2001 this course number was used for individual study in connection with LTI project research work and was a Pass/Fail course.) This course is for LTI PhD students only.
Pre-Requisites	Consent of Instructor.
11-920 - Independent Study: Breadth
Description	This is for graduate Independent study in language technologies.
Pre-Requisites	Consent of advisor. Special Permission is required to register.
11-925 - Independent Study: Area
Description	This course number is intended for individual study with the intended thesis advisor prior to acceptance of a student's thesis proposal.
Pre-Requisites	Consent of advisor. Special Permission is required to register.
11-927 - MIIS Capstone Project
Description	The capstone project course is a group-oriented demonstration of student skill in one or more areas covered by the degree. Typically the result of the capstone project is a major software application. The capstone project course consists of two components. The classroom component guides students in project planning, team management, development of requirements and design specifications, and software tools for managing group-oriented projects. The lab component provides project-specific technical guidance and expertise, for example in the development of a question answering system, dialog, or sentiment analysis application. Thus, each project receives two types of supervision, often from two separate members of the faculty.
11-928 - Masters Thesis I
Description	This course number is intended for last semester Masters students who wish to do an optional Masters Thesis.
Pre-Requisites	Consent of advisor.
11-929 - Masters Thesis II
Description	This course number is intended for last semester Masters students who wish to do an optional Masters Thesis. The student will normally have taken 11-925 - Independent Study: Area of Concentration for 12 units in the preceding semester, to produce an MS Thesis Proposal.
Pre-Requisites	Consent of advisor.
11-930 - Dissertation Research
Description	This course number is intended for PhD dissertation research after acceptance of a student's PhD thesis proposal. This course is for LTI PhD students only.
Pre-Requisites	Consent of advisor.
11-932 - Teaching Experience
Description	This course is for LTI PhD students only. This will be offered as a better way to assess their TA progress.
11-935 - LTI Practicum
Description	This course number is used for students who are on an internship as part of their graduate degree.
11-962 - Introduction to Machine Learning
Description	The course introduces students to the theoretical foundation of elementary Machine Learning (ML) concepts and algorithms, with a focus on how they relate to the field of Natural Language Processing (NLP). Students who complete this course will be prepared for continued graduate education in the areas of Data Science and Artificial Intelligence, and in particular we prepare students for advanced courses in Large Language Models and related topics. Students become skilled in evaluating the machine learning models (e.g. Decision Trees, Perceptron, Neural Networks, Deep Learning) that are well suited to supervised learning tasks, unsupervised learning tasks, and reinforcement learning tasks via a first principles approach (e.g. Information Entropy to motivate Decision Tree selection). Students develop practical skills with hands on coding assignments (e.g. modeling, training, testing, hyperparameter cross validation, visualization, error analysis) embedded in every aspect of the course to balance theoretical understanding with practical skills (e.g. support Gradient Descent theory with mini-batch Stochastic Gradient Descent and scheduled learning). Students will develop science communication skills required to effectively communicate their findings via data visualization and comprehensive reporting.
11-967 - Large Language Models: Methods and Application
Description	This course provides a broad foundation for understanding, working with, and adapting existing tools and technologies in the area of Large Language Models like BERT, T5, GPT, and others. It begins with a short history of the area of language models and quickly transitions to a broad survey of the area, offering exposure to the gamut of topics including systems, data, data filtering, training objectives, RLHF/instruction tuning, ethics, policy, evaluation, and other human facing issues. Students will delve into Transformer architectures more broadly and how they work, as well as exploring the reasons why they are better than LSTM-based seq2seq, decoding strategies, etc. Students will learn through readings and hands-on assignments where they will explore techniques for pretraining, attention, prompting, etc. They will then apply these skills in a semester-long course project, making use of locally sourced model instances that offer the opportunity to explore behind the curtain of commercial APIs. This is a certificate course and is only opened to certificate seeking students.
11-973 - Foundations of Computational Data Science
Description	This course provides an introduction to foundational concepts, learning material, and projects related to the three core areas of Data Science: Computing Systems, Analytics, and Human-Centered Data Science. Students completing this class will be prepared for further graduate education in Data Science and/or Artificial Intelligence. Students acquire skills in solution design (e.g., architecture, framework APIs, cloud computing), analytic algorithms (e.g., classification, clustering, ranking, prediction), interactive analysis (Jupyter Notebook), applications to data science domains (e.g., Natural Language Processing, Computer Vision) and visualization techniques for data analysis, solution optimization, and performance measurement on real-world tasks. This course is a remote course which is designed specifically for remote students in a certificate program.
11-977 - Multimodal Machine Learning
Description	Multimodal machine learning (MMML) is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages. With the initial research on audio-visual speech recognition and more recently with language vision projects such as image and video captioning, this research field brings some unique challenges for multimodal researchers given the heterogeneity of the data and the contingency often found between modalities. The course will present the fundamental mathematical concepts in machine learning and deep learning relevant to the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation & mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. These include, but not limited to, multimodal auto-encoder, deep canonical correlation analysis, multi-kernel learning, attention models and multimodal recurrent neural networks. We will also review recent papers describing state-of-the-art probabilistic models and computational algorithms for MMML and discuss the current and upcoming challenges. The course will discuss many of the recent applications of MMML including multimodal affect recognition, image and video captioning and cross-modal multimedia retrieval. This is a remote offering only available to certificate seeking students.
11-999 - Special Topics in Language Technology
Description	No course description provided.
11-324 - Human Language for Artificial Intelligence
Description	An enduring aspect of the quest to build intelligent machines is the challenge of human language. This course introduces students with a background in computer science and a research interest in artificial intelligence fields to the structure of natural language, from sound to society. It covers phonetics (the physical aspects of speech), phonology (the sound-structure of language), morphology (the structure of words), morphosyntax (the use of word and phrase structure to encode meaning), syntactic formalisms (using finite sets of production rules to characterize infinite configurations of structure), discourse analysis and pragmatics (language in discourse and communicative context), and sociolinguistics (language in social context and social meaning). Evaluation is based on seven homework assignments, a midterm examination, and a final examination.
11-423 - ConLanging: Learning About Linguistics and Language Technologies Through Construction of Artificial Languages
Description	Students will work individually or in small groups to create artificial human(oid) languages for fictional human cultures or SciFi worlds. Students will implement language technologies for their languages. In the course of creating the languages, students will learn about the building blocks of human language such as phones, phonemes, morphemes, and morpho-syntactic constructions including their semantics and pragmatics. Class instruction will focus specifically on variation among human languages so that the students can make conlangs that are not just naively English-like. We will also touch on philosophical issues in philosophy of language and on real-world socio-political issues related to language policy. Students will be required to use at least one of the following technologies: language documentation tools that are used for field linguistics and corpus annotation, automatic speech recognition, speech synthesis, morphological analysis, parsing, or machine translation. Learning Objectives: 1. The building blocks (phonemes, morphemes, etc.) of language, how languages are built from them, and how they interact 2. Metalinguistic awareness and knowledge about variation in human language 3. Language, thought, and culture: how does language reflect thought and culture, and vice versa. Why wouldn't Elvish be a good language for Klingons? 4. Language policy in the real world: For students who want to manipulate real languages. 5. Historical linguistics and language change: for students who want to manipulate real languages or make families of related conlangs for fictional worlds. 6. Practical experience with a language technology.
11-492 - Speech Technology for Conversational AI
Description	This course provides both practical and theoretical knowledge on how we can leverage speech processing technologies to build a conversational AI system. The course encompasses speech recognition, speaker recognition, speech synthesis, speech enhancement, speech translation, spoken dialogue systems, speech foundation models, and other speech and audio processing tasks. In practical sessions, students will learn to build functional speech recognition and synthesis systems or utilize existing large speech and language models and integrate them to create a speech interface using existing toolkits. The course will also present details of algorithms, techniques, evaluation metrics, and limitations of state-of-the-art speech systems. This course is particularly designed for students who want to learn how to process actual data for real-world applications, applying AI and machine learning techniques while also being aware of the current technology limitations.
11-601 - Coding Boot-Camp
Description	This course has one goal: To ingrain as deep a mastery of fundamental algorithm and coding skills as possible in the timeframe of this course. We will seek to specifically to maximize your chances of superior performance in any coding interview, improving your ability to form structured thoughts with respect to algorithmic problem solving, improve your ability to describe and plan solutions to problems, and develop further your ability to translate your thoughts into code intuitively and explain that code to others.
11-623 - ConLanging: Learning About Linguistics and Language Technologies Through Construction of Artificial Languages
Description	Students will work individually or in small groups to create artificial human(oid) languages for fictional human cultures or SciFi worlds. Students will implement language technologies for their languages. In the course of creating the languages, students will learn about the building blocks of human language such as phones, phonemes, morphemes, and morpho-syntactic constructions including their semantics and pragmatics. Class instruction will focus specifically on variation among human languages so that the students can make conlangs that are not just naively English-like. We will also touch on philosophical issues in philosophy of language and on real-world socio-political issues related to language policy. Students will be required to use at least one of the following technologies: language documentation tools that are used for field linguistics and corpus annotation, automatic speech recognition, speech synthesis, morphological analysis, parsing, or machine translation. Learning Objectives: 1. The building blocks (phonemes, morphemes, etc.) of language, how languages are built from them, and how they interact 2. Metalinguistic awareness and knowledge about variation in human language 3. Language, thought, and culture: how does language reflect thought and culture, and vice versa. Why wouldn't Elvish be a good language for Klingons? 4. Language policy in the real world: For students who want to manipulate real languages. 5. Historical linguistics and language change: for students who want to manipulate real languages or make families of related conlangs for fictional worlds. 6. Practical experience with a language technology. http://tts.speech.cs.cmu.edu/11-823/
11-630 - MCDS Practicum - Internship
Description	The MCDS Practicum course is used for recording CDS students summer internships for the MCDS Program.
11-631 - Data Science Seminar
Description	This course provides the MCDS students with a basic understanding of Data Science as an emerging scientific discipline, with interdisciplinary perspectives from computer science, business, and information policy / security. Students will read and discuss relevant publications, and attend presentations by guest speakers. Grading will be based on homework assignments related to the material covered in class sessions. This course is for MCDS students only.
11-632 - Data Science Capstone
Description	The MCDS-Capstone course is the final large team project for MCDS master degree students. Students take 11-632 as a combined capstone course for process & outcomes. This process & outcome course evaluates the project team's use of software engineering methodology and evaluates the outcomes of the project. Tools:Software Engineering best practices. Researching and developing innovative solutions to open problems. Tools are dependent on the specific project. General tools include: Github.
11-633 - MCDS Independent Study
Description	An independent study course is designed by the student to cover study of a particular area of interest too the student and is used when there is no formal course available in that subject area.
11-634 - MCDS Capstone Planning Seminar
Description	This course (open to MCDS students only) is a project based course where students exercise course work in a team based project. The course covers project management, a vision statement, requirements analysis, software architecture, functional specification, and an implementation of a prototype or scientific experiment. In addition, presentation skills are emphasized with multiple presentations throughout the semester.
11-635 - Data Science Capstone - Research
Description	The MCDS Capstone Research Course is the final large team project for MCDS master degree students. Students take 11-635 combined with 11-632 as combined capstone course for process & outcomes. This process and outcome course evaluates the project team's use of software engineering methodology and evaluates the outcomes of the capstone project. Tools:Software Engineering best practices. Researching and developing innovative solutions to open problems. Tools are dependent on the specific project. General tools include: Github.
11-651 - Artificial Intelligence and Future Markets
Description	This course focuses on applications of artificial intelligence and their role in shaping or disrupting new and existing markets. Students will work in teams to identify, analyze, and synthesize emerging trends and perform detailed studies of how these trends can influence and create markets. The teams will also assess artificial intelligence technologies and potential applications to create solutions that address identified market opportunities. A major objective of this course is for the teams to develop viable product ideas ultimately leading to a Capstone Project in the last semester of the MSAII program. For MS in Artificial Intelligence and Innovation (MSAII) students only.
11-654 - AI Innovation
Description	For MS in Artificial Intelligence and Innovation (MSAII) students only.
11-692 - Speech Technology for Conversational AI
Description	This course provides both practical and theoretical knowledge on how we can leverage speech processing technologies to build a conversational AI system. The course encompasses speech recognition, speaker recognition, speech synthesis, speech enhancement, speech translation, spoken dialogue systems, speech foundation models, and other speech and audio processing tasks. In practical sessions, students will learn to build functional speech recognition and synthesis systems or utilize existing large speech and language models and integrate them to create a speech interface using existing toolkits. The course will also present details of algorithms, techniques, evaluation metrics, and limitations of state-of-the-art speech systems. This course is particularly designed for students who want to learn how to process actual data for real-world applications, applying AI and machine learning techniques while also being aware of the current technology limitations.
11-723 - Linguistics Lab
Description	Self designed lab.
11-805 - Socio-technical Evaluations of Generative AI
Description	This course aims to introduce students to the growing number of evaluation metrics, measures, and methods proposed for assessing the capabilities and safety risks of Generative AI systems when they are deployed in society in ways that impact human lives and wellbeing. The course will also provide a methodological framework for designing and evaluating existing evaluation metrics, methods, and approaches. We formalize the necessary characteristics of evaluation benchmarks and automated methods to ensure that they capture the benefits and risks of GenAI systems in deployment. We focus on four key criteria: Validity, Reliability, Feasibility, and Usability. Through course projects, students will be prompted to design and/evaluate methods for evaluating the risks and capabilities of GenAI applications.