The class focuses on developing problem solving skills using Python as a programming language. Starting from procedural function development, we also explore object-oriented techniques, and discuss simple data structures that are often used in software development. The students usually do a few programming assignments, take a midterm, and submit a final project.

In this course, you will gain exposure to legal reference questions and the reference interview, investigate legal research sources, and beome familiar with the methods and strategies for conducting legal reference and basic legal research.

With the ongoing evolution of digital technologies, the creation and sharing of scholarly knowledge continues to change rapidly. In this course, we will explore historical developments, current issues, and ongoing debates in scholarly communication. We will also examine the critical roles of academic libraries and library professionals in the complex scholarly communication landscape. As we learn about topics such as academic publishing, open access and open scholarship, peer review, metrics and impact, copyright and fair use, open education, library values, and social justice, we will consider challenges and opportunities for librarians engaged in scholarly communication. In addition to building a broad understanding of key issues and areas of scholarly communication, students will develop more in-depth knowledge of a scholarly communication issue.

Examine inquiry models and information-seeking theories relevant to K-12 teaching and learning. Explore tools and resources for student learning and strategies for teaching specific information literacy skills within the context of a research-based inquiry.

History and ongoing evolution of instruction in library and information service settings; conceptions of information literacy; learning theories and pedagogical approaches; instructional design principles, including backward design; and reflective teaching practice.

Introduction to the concepts of information organization, representation, and classification. Consideration of different traditions of practice and user concerns.

Philosophical and social context, objectives, and methodology of evaluating, selecting, and managing library materials.

This course explores the life cycle of a digital library/collection through various critical lenses and hands-on experiences. The objective of the course is for students to obtain a solid understanding of the theoretical frameworks, technical processes, and technologies needed to build meaningful, ethical, and reusable digital libraries.

Explore common data collection, management, and sharing practices in information technology and emerging technologies, such as search engines and AI systems. Students will read papers and engage in discussions about the pros and cons of established data practices and learn about the three main components of responsible data management: 1) consent and ownership, 2) privacy and anonymity, and 3) broader impact. Students will also practice how to collect data, make data-driven decisions, and design data-driven products through group projects as UX designers, researchers, and data scientists. The course will bring in interdisciplinary perspectives with guest speakers from archive science, engineering, and respponsible AI, to provide a holistic view of broader data ecosystems and infrastructures.

Introduction to structures and practices within the rapidly-evolving field of librarianship. prepares students for their graduate work here.

This course examines key issues, challenges, and opportunities in the creation, management, and leadership of nonprofit organizations. Attention is given both to internal organizational issues and to nonprofits’ relationships with key external constituencies. Among the topics to be considered are nonprofit creation, mission management, organizational leadership, funding strategies, partnerships, and the impact of the public policy environment. Readings and discussion will examine nonprofits in varied fields of activity (such as human services and culture). Assignments and exercises will be employed to help develop presentation and grant writing skills. The course is organized in a seminar format and will employ exercises and cases to translate broad themes to practical issues related to nonprofit strategy and management. Guest speakers will periodically join us to share their own experiences building and running nonprofits (additional speakers may be added).

This class explores various data science models, both traditional and the state of the art techniques. The course is designed to provide mathematical and computational basis such as Linear Algebra, Optimization techniques, and probabilistic modeling for different types of machine learning models. The goal of the class is provide a foundational basis for data science techniques. The class focuses on PSETs and a final data science project.

Develop prompts for text and image generation through an iterative cycle, making the most of foundation models, including large language models and diffusion models. Overview of the field of prompt engineering, including historical development, ethical dilemmas, and the creation of chatbots.

Disaster events, like floods and fires, can cause severe damage and loss in cultural heritage collections. How will you respond when disaster strikes? This course introduces students to the fundamental preservation concepts, planning strategies, and applied response techniques that make a difference. The class will evaluate the physical impact of fire, water, soot, mold, and insects on varied media, including books, flat paper, audiovisual materials, and other items. Disaster planning exercises model proactive methods to minimize damage. Hands-on response drills enable students to practice salvage techniques, and to triage and prioritize impacted materials.

The current Web has experienced tremendous changes to connect data, people, and knowledge. There are a couple of exciting efforts trying to bring the Web to its full potential. The Semantic Web is one of them which is heavily embedded in the Artificial Intelligence area with the long-term goal to enhance the human and machine interaction by representing data semantics, integrating data silos, and enabling intelligent search and discovery. This course aims to provide the basic overview of the Semantic Web in general, and data semantics in particular, and how they can be applied to enhance data integration and knowledge inference. Ontology is the backbone of the Semantic Web. It models the semantics of data and represents them in markup languages proposed by the World Wide Web Consortium (W3C). W3C plays a significant role in directing major efforts at specifying, developing, and deploying standards for sharing information. Semantically enriched data paves the crucial way to facilitate the Web functionality and interoperability. This course contains three parts: Semantic Web language, RDF graph database (i.e., RDF triple store), and its applications. The fundamental part of the course is the Semantic Web languages. It starts from XML and goes further to RDF and OWL. The RDF graph database part introduces different APIs of Jena and its reasoners. The application part showcases current trends on semantic applications. Prerequisites Basic knowledge of HTML and XML is desired. Course Objectives This course aims to develop a critical appreciation of semantic technologies as they are currently being developed. At the end of this course, students should be able to: • sketch the overall architecture of the Semantic Web. • identify the major technologies of the Semantic Web and explain their roles. • illustrate the design principles of the Semantic Web by applying the technologies. • understand certain limitations of the Semantic Web technologies, and be aware of the kinds of services it can and cannot deliver. Course aims are achieved through: • Lectures covers basic knowledge of the Semantic Web • Projects applying semantic technologies to concrete problems of information delivery and use • Assignments of practicing and utilizing key semantic technologies

The purpose of this course is to provide theoretical and practical foundations for information professionals who wish to design and evaluate search systems and services, taking user-centered approaches. This course explores search user interfaces, search behavior, search interaction, search user experience, search as learning, search for creativity, and research methods for understanding information behavior and evaluating search systems. Students will learn search behavior across various contexts, including academic and professional settings, everyday life, and digital learning environments. Students will gain insights into how people interact with, use, and evaluate information in a variety of application areas, such as web search engines, domain-specific search systems, digital libraries, social search platforms, and generative AI-based systems.

This course will cover fundamental concepts in Machine Learning (ML). The course will provide conceptual and practical knowledge on a wide range of modern machine learning algorithms; including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), reinforcement learning & deep learning models (CNN, RNN, Autoencoders & Transformers) and also introduce the importance of Prompt Engineering and Retrieval Augmented Generation. The goal is for students to be comfortable and confident in machine learning concepts and have the ablity to build machine learning model solution to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, this is a great place to start.

Natural Language Processing (NLP) is concerned with interactions between computers and humans through the medium of human languages. It involves analyzing, understanding, and generating human language, making it possible for machines to interpret and respond to human speech and text. NLP is currently making significant contributions to modern technological advancements and serves as the backbone of crucial applications such as Gen AI, Conversational AI, Question Answering, Human Language Translation, Summarization, Sentiment and Emotion Analysis, Search and Recommendation, and Information Extraction in various domains such as healthcare, finance, legal, libraries and education and beyond. The proposed graduate-level course aims to cover fundamental concepts in Natural Language Processing / Computational Linguistics and how they are used to solve real-world problems. Classes in each week will be divided into two segments: (a) Theory and Methods, a concise description of an NLP concept, and (b) Practicum, a hands-on session on applying the theory to a real-world task on publicly available multilingual text datasets. We will use Python for programming along with popular libraries for text processing such as NLTK, SpaCy and HuggingFace's transformers. By the end of the course, the goals for the students are to: 1. Understand the process of garnering and pre-processing a large amount of multilingual textual data from various domains and sources. Characterize the processes to store, load, pre-process multilingual data and apply language processing operations such as normalization, tokenization, lemmatization, chunking and machine readable representation (vector) extraction. 2. Train machine learning algorithms for natural language understanding and generation and evaluate their performance. 3. Learn to extract information from unstructured text and represent them in the form of knowledge graphs 4. Learn to use existing knowledge graphs, ontologies and lexical knowledge networks for predictive analysis on text 5. Learn about popular NLP applications and tasks and the process of building such applications 6. Propose a novel product/research-focused idea (this will be an iterative process), design and execute experiments, and present the findings and demos to a suitable audience (in this case, the class).

Learning key data wrangling maneuvers in abstract and implementations in SQL, Excel, R Tidyverse, and Python Pandas. Maneuvers in data transformations include Nest, Pivot, Mutate (inc. separate/unite), Group/Summarize and Rectangling. Projects include working with "wild caught" data datasets (usually CSV or JSON) and computational notebook environments (e.g., iPython, Jupyter, Rmarkdown, Quarto). Fall 2024 has changes from previous syllabus now that we have Database Design and Introduction to Programming. Nonetheless, the previous syllabus is still useful as it links to course materials that show the teaching approach and type of assignments. http://howisonlab.github.io/datawrangling/#Schedule_of_classes

Introduction to combining human and machine intelligence to benefit people and society. Explore cutting-edge research on a number of subjects related to human-AI interaction, including the psychological and societal impacts of AI as well as design guidelines and methods for human-centered AI.

Exhibits are a powerful way for libraries, archives, museums, and cultural institutions to engage the public with their collections. This course offers students the opportunity to plan and install an exhibit, focusing on objectives such as: crafting a narrative around physical objects; drafting exhibit text; accommodating media preservation issues; building basic display supports; and publicizing the exhibit. Students will learn about the historical origins of modern-day exhibit practices and will visit and evaluate current exhibits on campus and in the Austin area.

Designed for students pursuing Texas Education Agency (TEA) certification in school librarianship. Examine the philosophy, objectives, and management of the school library with an emphasis on standards and competencies, and the roles of the school librari

Designed for students pursuing Texas Education Agency (TEA) certification in school librarianship. Examine the philosophy, objectives, standards, and management of the school library with an emphasis on the roles of the school librarian as an instructiona

This course will develop your skills to effectively manage a library, or information organization. We’ll be looking at problems faced by many types of libraries: public, academic, school, special. We’ll examine staffing, budget, collection development, patron behavior, and managing the expectations of users.

Designed for students pursuing Texas Education Agency (TEA) certification in school librarianship. 160 hours of field work in varied school library settings under the supervision of a qualified field supervisor and site supervisor.Offered on the credit/no credit basis only. Participants will not ordinarily receive monetary compensation. Additional information about the Practicum in School Libraries can be found at https://ischool.utexas.edu/programs/school-librarian-certificate/practicum

Study of audio recording through a chronological examination of the development of recording; basic care and preservation of recordings; Preservation of audio archives; and stability concerns of audiovisual media.

Ever wondered how libraries and archives safeguard historical materials for future generations? Preservation is the answer. In this course, students learn collections care strategies that enable today’s information stewards to protect our growing cultural record. Scientific foundations and practical exercises will address common preservation challenges, such as environmental control, mold, insects, pollutants, and light damage. Modern topics in health, safety, and sustainability will highlight the developing nature of the field. Students will evaluate preservation risks for books, paper, electronic media, and other collections materials.