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Curriculum

Curriculum
[KSE521] Business Intelligence
Business intelligence plays a pivotal role in turning a large set of data into information and knowledge for effective decision making. This course covers the fundamental concepts and skills associated with major business intelligence applications including database, data warehouse, and data mining.
[KSE522] Knowledge System Modeling and Design
This course will examine the nature and principles of knowledge systems from performance and methodology perspectives. It will also cover the software engineering modeling concept and basic elements of knowledge systems using the specific programming language UML and JESS.
[KSE523] Knowledge Service Design Using Web Technologies
The students will learn knowledge service design based on Web technologies and will develop a knowledge service project during the course. The course will highlight the features of different Web Services Technologies and introduce various Scripting Languages, provide an up-to-date survey of developments in Web Services Technologies, and present techniques to support real-time software development.
[KSE524] Information Search and Management
This course will cover traditional material as well as recent advances in information retrieval (IR), the study of the indexing, processing, and querying of textual data. Students will learn tools and techniques to do research in the area of information search and management.
[KSE525] Data Mining and Knowledge Discovery
This course teaches the basic concepts and methods of data mining. More specifically, frequent patterns and associations; classification and prediction; and cluster analysis will be covered. The main goal of this course is to give the students a broad knowledge of various data mining methods without confining to a specific domain. This course is intended as a prerequisite for advanced data mining courses and thus is suitable for both undergraduate and graduate students.
[KSE526] Analytical Methodologies for Big Data
This course discusses basic analytical methodologies for big data, which are vital to data scientists. Big data analytics calls for extending existing algorithms so that they can support big data. In this course, the instructor will first teach MapReduce, which is the representative framework of processing big data, and then the methodologies of extending the algorithms—mostly for text retrieval/processing and graph analysis—into MapReduce. The students will also learn how to implement those algorithms using Apache Hadoop. As a result, the students will achieve the basic capabilities needed to design the algorithms of big data analytics.
[KSE531] Human-Computer Interaction: Theory and Design
This course acquaints the students to principles and practice in human-computer interaction design. The context of computer supporting of human decision-making tasks is emphasized. Based on relevant background knowledge from the perspectives of cognitive science, information design, and human factors engineering, more specific topics including task-based design methodologies, cognitive task analysis, strategy analysis, and information aiding and visualization are taught.
[KSE611] Introduction to Learning Science
The purpose of this class is to expose students to the foundational theoretical, technological, and methodological issues underlying previous work in learning science. In addition, the class introduces students to the wide range of current learning environments for formal and informal interaction and learning on-line, and explores current research in improving the quality of experiences these environments have to offer.
[KSE612] Human Decision Making and Support
Types, strategies, limitations, and models of human decision making are considered. Human problem solving strategies and heuristics in choice, estimation, and diagnosis problems are analyzed. Also discussed are various intelligent approaches and systems to support the human strategies providing timely and well-designed information.
[KSE621] Advanced Techniques in Information Retrieval and Data Mining
This course covers emerging advanced topics in Information Retrieval (IR) and Data Mining (DM). For the topics selected, students will not only learn about the most recent research progress, but also have an opportunity to practice skills for doing research and identifying interesting new research directions.
[KSE622] Soft-computing in Intelligent System Design
The first objective is to learn what kind of role precision and imprecision have in engineering and engineering system design. The second objective is to understand the need to use soft-computing in designing intelligent systems. The third objective is to have a basic understanding of different kinds of soft-computing methodologies as well as hybrid methodologies. The fourth objective is to design and build a fully functional Fuzzy Logic Controller / fuzzy application in a real world project case.
[KSE623] Knowledge Structure and Modeling
Knowledge structure is an interrelated collection of facts or knowledge about a particular topic. It is composed of concepts linked to other concepts by labeled relationships. The course involves modeling of knowledge structure using XML, RDF, and ontology using the semantic Web as a knowledge source.
[KSE624] Mobile and Pervasive Computing for Knowledge Services
Over the past decade, there has been an increasing trend towards integrating sensing, communication, and computation into the physical world, from electronic toys to cars, from augmented classrooms to smart homes. In this course, we will take an interdisciplinary look at current research topics in mobile and pervasive computing by reading and discussing recent literature and discuss how we can provide intelligent knowledge services using mobile and pervasive computing.
[KSE625] Data Mining for Social Networks
This course teaches key concepts and algorithms for analyzing online social networks from the data mining point of view. The course will cover many interesting topics including community discovery, evolution analysis, link prediction, and influence analysis. The instructor will introduce the representative papers (two for each week) published in the data mining field. In addition, the students will get to play with real data crawled from social networking sites.
[KSE631] Content Networking
Today’s Internet is all about content that ranges from movie titles (e.g., Netflix) to user generated content (e.g., Youtube and Twitter). Further, ever increasing popularity of the mobile Internet has dramatically changed business and technologies for content networking. In this course, we review enabling technologies for the mobile Internet and content networking and discuss state-of-the-art content networking research issues such as mobility, context awareness, and social networking.
[KSE641] Cognitive Engineering
Approaches to enhance overall performance in complex human-machine cognitive systems are considered. The engineering methods to design system intelligence and interaction are discussed in an systems engineering point of view, covering prescriptive and descriptive models of human and machine intelligence, analysis and design of task-function complexes, and some application-oriented issues.
[KSE643] Knowledge Engineering and Intelligent Decision Making
Knowledge engineering plays a key role in integrating knowledge into computer systems for intelligent decision making. This course covers the fundamental concepts, methods, and tools related to knowledge engineering and applies them to the Web for the design of intelligent decision making systems.
[KSE652] Social Computing Systems Design and Analysis
The purpose of this course is to review recent social computing systems (e.g., human computation, crowdsourcing, social Q&A, and social recommendation) and to investigate social computing systems design issues (e.g., incentives, quality of user contribution, sustainability). To this end, students will take an interdisciplinary look at current research topics in social computing systems design and analysis by reading and discussing recent literature.
[KSE653] Service UX Design
The purpose of this class is to introduce the definition of service design and experience design as well as related theoretical, technological, and methodological issues underlying previous work in service design. Students will explore current theory and cases, as well as research trends that are needed in order to design the “invisible” service. In addition, students will actively participate in hands-on design practices by learning and applying methods learned in class.
[KSE801] Special Topics in Knowledge Service Engineering I
This course is offered to meet the ad hoc demand of students in special areas of Knowledge Service Engineering which is not covered by regular courses.
[KSE808] Invited Lecture I
This course is offered to meet the ad hoc demand of students in special areas of Knowledge Service Engineering which is not covered by regular courses.
[KSE809] Invited Lecture II
This course is offered to meet the ad hoc demand of students in special areas of Knowledge Service Engineering which is not covered by regular courses.
[KSE960] MS Thesis
This listing is for participation in advanced research under the direction of a faculty member.
[KSE966] Seminar in MS
Regularly held seminars on up-to-data topics help M.S. students grasp the current direction of development and applications in the general Knowledge Service Engineering areas.
[KSE980] Ph.D Thesis
This listing is for participation in advanced research under the direction of a faculty member.
[KSE986] Seminar in Ph.D
Regularly held seminars on up-to-data topics help Ph.D. students grasp the current direction of development and applications in the general Knowledge Service Engineering areas.