Online M.S. Human Language Technology
The online HLT program at the University of Arizona is designed to accommodate working professionals seeking a career at the intersection of AI and language technology. Each course in the program is delivered over 7.5 weeks. As many are working full-time, our students typically take one course at a time in a back-to-back fashion.
Because the program is open to students around the world, the courses do not involve any live lectures. Instead, all content is delivered asynchronously in a staggered fashion with weekly deadlines to encourage continuous progress. This means that you need not adjust your daily schedule to accommodate class times. Unlike our in-person program, courses in the fully online MS are offered in the fall, spring, and summer.
Degree Requirements
Coursework requirements for the M.S. in Human Language Technology program are the same in-person and online.
Required Courses
Fundamentals of formal language theory; syntactic and semantic processing; the place of world knowledge in natural language processing. Graduate-level requirements include a greater number of assignments and a higher level of performance.
This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models. Graduate-level requirements include assignments of greater scope than undergraduate assignments. In addition to being more in-depth, graduate assignments are typically longer and additional readings are required.
Topics include speech synthesis, speech recognition, and other speech technologies. This course gives students background for a career in the speech technology industry. Graduate students will do extra readings, extra assignments, and have an extra presentation. Their final project must constitute original work in a speech technology.
This course provides a hands-on project-based approach to particular problems and issues in computational linguistics.
Specialized work on an individual basis, consisting of training and practice in Human Language Technology in a academic, technical, business, or governmental establishment.
An introduction to syntactic theory with an emphasis on data analysis, critical thinking, and theory development. Taught within the generative Principles and Parameters approach to syntax. Graduate-level requirements include a greater number of problems.
Elective Courses
This course presents an overview and understanding of the intractable and pressing ethical issues as well as related policies in the information fields. Emerging technological developments in relation to public interests and individual well-being are highlighted throughout the course. Special emphasis is placed on case studies and outcomes as well as frameworks for ethical decision-making.
This course will introduce students to the concepts and techniques of data mining for knowledge discovery. It includes methods developed in the fields of statistics, large-scale data analytics, machine learning, pattern recognition, database technology and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Topics include understanding varieties of data, data preprocessing, classification, association and correlation rule analysis, cluster analysis, outlier detection, and data mining trends and research frontiers. We will use software packages for data mining, explaining the underlying algorithms and their use and limitations. The course include laboratory exercises, with data mining case studies using data from many different resources such as social networks, linguistics, geo-spatial applications, marketing and/or psychology
This course provides an overview of the various concepts and skills required for effective data visualization. It presents principles of graphic design, programming skills, and statistical knowledge required to build compelling visualizations that communicate effectively to target audiences. Visualization skills addressed in this course include choosing appropriate colors, shapes, variable mappings, and interactivity based on principles of color perception, pre-attentive processing, and accessibility.
A survey of the aims of linguistic research and introduction to the basic mathematics of formal linguistics; logic, sets, algebra, graphs, feature structures, formal language theory.
A continuation of LING 503, Foundations of Syntactic Theory I, taught within the Minimalist approach to syntactic theory, with a focus on principles of theory construction and empirical issues in binding, control, movement, structure, and the interfaces with semantics and morphology.
A continuation of 503, this class compares alternative non-Chomskyan theoretical approaches to syntactic theory. Including Relational Grammar, Head Driven Phrase Structure Grammar, Categorical Grammar and Lexical Functional Grammar.
Students will learn to use the statistical methods common in linguistics and related fields in order to apply them in the design and analysis of their own research. Methods covered will include ANOVA, ANCOVA, correlation, regression, and non-parametric tests, as well as some specialized analyses such as Multidimensional Scaling Analysis. The course will focus primarily on methods and problems of psycholinguistic, phonetic, and sociolinguistic research. Discussion of the statistical analyses in published articles in these areas will form a substantial part of the course, and application of the methods covered in the course to the students' own research will also be discussed. The course will include instruction in use of statistical software packages.
Students are introduced to computer programming as it pertains to collecting and analyzing linguistic data. The particular programming language is chosen at the discretion of the instructor. Graduate-level requirements include more challenging exams; 50% greater contribution to their respective group projects; 9 instead of 6 assignment; additional readings from the primary literature.
Investigation of the principles that underlie current phonological theory, concentrating on the representation of sounds and the regular patterns of sound in natural language. Topics include distinctive feature theory, syllable theory, the core skeleton, rule formulation and rule interactions. Graduate-level requirements include a greater number of problems.
Investigation of the evidence and arguments for non-linear representations (autosegmental and metrical) and of the organization of the phonological component of grammar, including evidence for its interaction with morphological structures and rules.
Study of the acoustic and articulatory properties of sounds and patterns of sounds that occur in human language. Focus on the significance of the properties of sounds for phonological theory, in particular, distinctive feature theory. Role of psycho-acoustic studies as a source of evidence for phonological theory. Graduate-level requirements include an additional project or research paper.
Study of word and sentence meaning, relationship between the lexicon and the grammar, idioms, metaphor, etymology, and change of meaning.
This class serves as an introduction to human language technology (HLT), an emerging interdisciplinary field that encompasses most subdisciplines of linguistics, as well as computational linguistics, natural language processing, computer science, artificial intelligence, psychology, philosophy, mathematics, and statistics. Content includes a combination of theoretical and applied topics such as (but not limited to) tokenization across languages, n-grams, word representations, basic probability theory, introductory programming, and version control.
Course Description (no char. limit): This intermediate-level course is a continuation of LING 529 and covers a combination of theoretical and applied topics such as (but not limited to) unsupervised learning (clustering), decision trees, and the basics of information retrieval.
Introduction to language processing. The psychological processes involved in the comprehension and production of sounds, words, and sentences. Other topics may include language breakdown and acquisition, brain and language, and bilingual processing. Graduate-level requirements include more extensive readings and writing.
This course focuses on the major theories of language development, including nativism and various forms of learning. Students read and discuss primary source material written by linguists, psychologists, and other cognitive scientists who work in the field of language acquisition.
Morphology is the internal structure of words and the relationship between words and the syntactic, phonological, and semantic properties of the units that include them. Course work includes the development of morphological theory.
An examination of the syntactic diversity presented by natural human languages and an exploration of the issues that such diversity presents for syntactic analysis. Topics include AUX, word order, constituency, and subjects.
Introduction to model-theoretic investigations of natural language interpretation, including coordination, quantification, referential relations, tense, aspect and modality.
This course focuses on statistical approaches to pattern classification and applications of natural language processing to real-world problems.
The development and exchange of scholarly information, usually in a small group setting with an in depth investigation of computational linguistics theory and application. The scope of work shall consist of research by course registrants, with the exchange of the results of such research through discussion, reports, and/or papers.
Internship
In addition to program coursework, students are expected to complete a minimum of 6 credit hours of internship. The internship requirement can be met any point during the program, though we typically suggest waiting until finishing the first 2-4 courses. The internship experience provides real-world training in preparation for a career. We're very flexible about the form the internship can take (project at a startup, academic institution, non-profit, intelligence agency, etc.).
For more information, please look at our frequently asked questions.
More Information
For more information, please visit the University of Arizona's Online Campus program page.