Informatics Courses
Inf 501 Case Studies in Digital Citizenship (3)
The purpose of this course is for students to explore topics related to digital citizenship through the close examination of case studies. Students will be asked to look to current issues and cases involving digital citizenship and apply themes, such as the ethical use of information, in their examination and discussion of them. Students who have received credit for CINF401 cannot receive credit for this course.
Inf 504 Advanced Systems and Security (3)
This course is designed to provide an advanced coverage of systems with a specific focus on cyber security. Engineered security is examined through the application and introduction to authentication protocols and intrusion detection for Unix, Windows and databases and general software security. Also considered are security issues related to people's use of systems including policies and practices for password management and protecting privacy rights. Students also study options for maintaining business continuity in the event of a disruption of business operations. Security models such as Bell-LaPadula are introduced and studied. Specific case studies are used to highlight the choices that must be made to balance operational efficiency of business functions with protecting the business from the onslaught of security threats. Prerequisite: Some programming.
Inf 505 Advanced Concepts and Practices in Software Development (3)
A course in advanced software development techniques and practices. This will build on students' previous experience to enable them to create larger, more complicated projects. In addition to advanced language, library, etc. features, this course will emphasize concepts such as object-oriented design and development, software engineering, testing, design thinking, etc. These will increase the scale of projects students can achieve as well as increase their chances of successful development. Prerequisite(s): Recommended - Significant software development experience or Permission of Instructor. Students who have received credit for CINF405 cannot receive credit for this course.
Inf 507 Modern Issues in Databases (3)
This graduate course introduces the students to the emerging topics in database systems. This course is especially designed for students with emphasis on advanced concepts and algorithms in database systems, topics that are state-of-the-art research, or recent seminal contributions in the broad field of database and information systems and will include discussions of security and privacy of information data systems. Specific topics will vary. Prerequisite: IST 506 or permission of instructor. Students who have received credit for CINF407 cannot receive credit for this course.
Inf 508 (Bio 518, Gog 518) Ecological Modeling (3)
This course introduces various theoretical and mathematical approaches to modeling ecological and environmental data through computer-based exercises in the application of existing models and the development of new models. Modeling topics cover animal population models, vegetation models, and large scale landscape models, as well as model applications in decision making. This course is geared towards demystifying models and providing students with the confidence and skills to apply this very useful tool to research projects. Prerequisites: Statistics and either General Ecology, Environmental Analysis, Environmental Studies or equivalent or permission of instructor.
Inf 523 Fundamentals of Information Technology (1)
A university-wide offering that introduces fundamentals of information technology in an intensive graduate format. The course focuses on selected topics such as database applications, introduction to programming, web technologies, and Unix and networking that are offered in one credit modules, each lasting for half a semester.
Inf 528 Analysis, Visualization, and Prediction in Analytics (3)
Principles of data analysis, emphasizing modern statistical and machine-learning based approaches. Also, the important role of simple analyses and visualization to gain an overall understanding of data sets, regardless of size. The role of analytics in creating predictive models of phenomena. The importance of understanding the nature of the data and other methodological considerations. Prerequisites: Recommended - Some statistics and database experience. Students who have received credit for CINF428 cannot receive credit for this course.
Inf 538 Applied Machine Learning (3)
A technically-oriented course in the concepts and implementation of machine learning systems. It covers unsupervised and supervised approaches. A variety of algorithms are discussed as well as their applicability. The critical importance of data for machine learning is also covered. Student learning will include extensive implementation exercises and projects. Prerequisites: Recommended - Knowledge of statistics.
Inf 551 (Csi 551, Phy 551) Bayesian Data Analysis and Signal Processing (3)
This course will introduce both the principles and practice of Bayesian and maximum entropy methods for data analysis, signal processing, and machine learning. This is a hands-on course that will introduce MATLAB computing language for software development. Students will learn to write their own Bayesian computer programs to solve problems relevant to physics, chemistry, biology, earth science, and signal processing, as well as hypothesis testing and error analysis. Optimization techniques to be covered include gradient ascent, fixed-point methods, and Markov chain Monte Carlo sampling techniques. Prerequisites: Csi 201, Mat 214, or equivalents, or permission of instructor; Phy 509 or equivalent programming experience with permission of the instructor.
Inf 562 Current Technologies in Web Design (3)
Provides an advanced coverage of web design and development, with a focus on current technologies and processes. Students will develop skills on the use of software development practices such as agile development and test-driven development. Develop familiarity with current technologies in particular web-based and mobile applications. Prerequisites: Recommended - Some experience with web development and interactive user experience (IUX). Students who have received credit for CINF462 cannot receive credit for this course.
Inf 570 Physical Computing (3)
This course introduces programmable microcontrollers, digital chips that are used to control electronics and robotics projects. In this course students will simultaneously develop the electronic circuits and associated software for controlling hardware components including sensors and mechanical parts. Topics include electronics fundamentals, analog/digital (A/D) devices, pulse-width modulation (PWM) and embedded programming. Course has hands-on lab setting with a final group project.
Inf 585 IT and Homeland Security (4)
This course examines the political, legal and policy aspects of the use of information technologies by the US Department of Homeland Security (DHS), non-technological dimensions of information collection, use and management and the use of technologies other than computing in the homeland security domain. The course is focused on information technology use by the US federal government but will also examine state and local governments and other countries as well as international issues such as information sharing and international technical standards.
Inf 596 Advanced Special Topics in Informatics (3)
The contents of this course will vary from semester to semester. Each offering will cover an advanced topic in Informatics. May be repeated for credit when content varies. Students who have received credit for CINF496 cannot receive credit for this course.
Inf 597 Advanced Mini Special Topic in Informatics (1)
The contents of this course will vary from semester to semester. Each offering will cover an advanced topic in Informatics. May be repeated for credit when content varies.
Inf 624 Predictive Modeling (3)
Fundamental concepts and techniques to discover patterns in data, identify variables with predictive power, and to develop predictive models. Topics include statistical, data mining and machine learning concepts and methods: data selection, representation, cleaning and preprocessing; algorithms such as classification, clustering and association rules; advanced techniques such as deep learning, and text and web mining. Best practices on the selection of methods and tools to build predictive models. Prerequisite: Emh 650 or a graduate-level statistics course that covers basic statistics, including regression.
Inf 625 Data Mining (3)
An introduction to the concepts and techniques of data mining for knowledge discovery, using methods from statistics, data analytics, machine learning, pattern recognition, database, and artificial intelligence. These will be used for the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns. Topics include understanding the characteristics of data, data preprocessing, classification, association and correlation rule analysis, cluster analysis, outlier detection, and research frontiers in data mining. The course will explore both the underlying algorithms and their hands-on application in order to promote both in-depth understanding and experience in their use and limitations. Prerequisite: Ist 506.
Inf 626 Big Data and Stream Analytics (3)
In data science, the analysis of large amounts of data is frequently expressed as the 4 V's: volume, velocity, variety, and veracity. This course examines the underlying concepts and practical implications of each of these dimensions at the frontier of data analytics. The size and amount of time available to process data both affect the types of analysis that are possible, as does the variety of data. In addition, issues of data source, distribution, and how much it can be trusted as the basis for analysis are increasingly important. Prerequisite: Inf 624.
Inf 671 (Csi 671) Computer Vision (3)
Billions of images are hosted publicly on the web - how can you find one that "looks like" some image you are interested in? How can a robot identify objects in complex environments, or navigate uncharted territory? How can a video camera in the operating room help a surgeon plan a procedure more safely, or assist a radiologist in more efficiently detecting a tumor? Computer vision is at the heart of many such questions: the goal is to develop methods that enable a machine to "understand" or analyze images and videos, so that information can be derived from raw pixel values to support various applications. In this course, through lectures, paper presentations, and projects, we will explore fundamental topics including image formation, feature detection, segmentation, recognition and learning, and motion and tracking. We will treat computer vision as a process of inference from noisy and uncertain data and emphasize probabilistic, statistical, and data-driven approaches. Prerequisites: This course requires familiarity with calculus, basic probability theory and linear algebra, and some programming experience. Previous experience with image processing and machine learning will be useful but is not assumed. MATLAB, the language of choice for the programming assignments will be covered as part of the introduction to the course.
Inf 710 Research Design in Information Science (3)
Students will examine research issues in information science at an advanced level, focusing on appropriate research design, data gathering techniques and analysis relating to data collection and measurement. Students will explore the research design process from both qualitative and quantitative points of view. Offered in the spring only.
Inf 711 Research Seminar I (1)
This course is offered every fall for all first-semester students. The course meets once a week to hear presentations by faculty about their current research. In addition, research skills are developed, such as evaluation of information science literature, how to write a literature review, how to plan and use bibliographic software, and how to do a poster session at a conference.
Inf 712 Research Seminar II (1)
This course is offered every spring for all second-semester students. This course meets three times during the semester to plan and coordinate the INF Research Conference while also developing posters to present at the Research Conference. Students develop their research agenda by completing their INF Program Plan. Prerequisite: Inf 711
Inf 713 Research Seminar III (1)
This course is offered every fall for all third-semester students. This course meets weekly to hear presentations by faculty about their current research. Students develop research relationships with faculty to continue their own research. Prerequisites: Inf 711 and 712
Inf 714 Research Seminar IV (1)
This course is offered every spring for all fourth-semester students. This course meets three times during the semester to guide students’ independent research. Students present their research with a faculty member at the INF Research Conference. Prerequisites: Inf 710, 711, 712 and 713
Inf 720 Managing Information and Technology in Organizations (3)
This course will introduce information systems research paradigms grounded in organization theory and provide a framework for applying theoretical concepts and empirical tools to the management of information and technology in organizations.
Inf 721 Information and Society (3)
Relationships between information and communication technologies and social action; how social and organizational factors influence processes and systems, and how the use of ICTs influence our (changing) understanding and experience of dealing with information.
Inf 722 Information Organization (3)
Text analysis for information extraction, organization of information for knowledge sharing, and visualization of information to support users’ diverse cognitive styles.
Inf 723 Information and Computing (3)
Development of theories and concepts that underlie the operation of information processing and retrieval systems; consequences derived from these theories that should be considered in designing such systems; theoretical foundations of information and computation; technologies and application areas.
Inf 724 Information Policy (2)
National and international information policy development trends, processes, and conflicts; policy, law, and culture; information economics, industries, and trade; policies of information commodities (e.g. intellectual property, privacy)
Inf 894 Directed Readings in Information Science (1-4)
Supervised readings for doctoral students on a particular topic or significant problem in information science. Prerequisite: Admission to Information Science Ph.D. program or permission of Ph.D. program director.
Inf 897 Independent Study and Research in Information Science (1-6)
Independent study and research in information science at the doctoral level under the direction of a member of the faculty. Prerequisite: Admission to Information Science Ph.D. program or permission of Ph.D. program director.
Inf 899 Doctoral Dissertation (1)
Course grading is Load Only and does not earn credit. Appropriate for doctoral students engaged in research and writing of the dissertation. Prerequisite: Admission to doctoral candidacy.
