This course addresses problem solving and programming using problem-based learning; variables, control flow, iteration, modules, arrays, file processing, classes, and objects; and basic graphical-user interface concepts (forms/pages and controls) for desktop and/or Web or mobile environments. The course consists of three hours of lecture and three hours of laboratory per week.

This course is the first half of a two-semester course in discrete mathematics. Topics in the course include logic, sets, functions, numeric bases, matrix arithmetic, divisibility, modular arithmetic, elementary combinatorics, probability, graphs, and trees. There will be an emphasis on applications of mathematics.

Intelligent systems technologies that have or may become practical for organizational use will be addressed in this course. Topics may include simple expert systems and expert systems with certainty factors, case-based reasoning, machine learning, neural networks, genetic algorithms, fuzzy logic, and two-person game playing. (offered in alternate years)

This course studies how business systems work and examines challenges confronting business organizations in the information age and beyond. One major challenge is to efficiently and effectively use three most important organizational resources, information, technology, and people, to provide service and value. To meet this challenge, the course studies business systems and strategies that organizations can utilize to organize data into information and synthesize information into knowledge. The course examines design and development of relational database management systems using Microsoft Access (structured query language), decision support systems using Microsoft Excel (what-if analysis, pivot tables, and decision tree analysis), enterprise information systems using SAP (ERPsim), and web-based systems using Google Analytics. The concepts, models, and frameworks are derived from both academic and professional sources.

This course is about the manager's responsibilities for decision making in the Information Age using Decision Support Systems (DSS) and Expert Systems (ES). DSS topics include: Data Management, Modeling and Model Management, User Interface, Executive and Organizational Systems, Group Decision Support Systems (GDSS), and DSS Building Process and Tools, including Spreadsheets, Natural Language Programming, and Influence Diagramming. ES topics include: Applied Artificial Intelligence, Knowledge Acquisition and Validation, Knowledge Representation, Inferencing, and ES Building Process and Tools. Students are required to apply DSS and ES software packages in a hands-on environment.

This course is designed to introduce students to one of several areas of multi-disciplinary emerging trends in Business Systems and Analytics. Students will learn the fundamental principles and concepts of a specific topic, its applicable technology, the design and implementation of the systems that support the area of study, and methods for measuring efficacy. Evolving technologies will be addressed as appropriate, and their relevance to business pursuits will be discussed and analyzed. Lectures and case studies will be used to give the student a solid understanding of the topic. A group project to develop and present an area initiative/concept will be the capstone of this course. This course is offered under different titles and can be repeated for additional credit when taken as a different topic.

This course examines human consciousness. Topics include the relation between the mind and the brain, the possibility of building conscious machines, the mental life of animals, and conceptual puzzles posed by zombies.

The topics in this course include privacy and information use/misuse offline and online, intellectual property, the First Amendment, e-waste, accuracy of information, ethics, effects of computers on work and society, responsibilities and risks of computing, current issues such as credit cards and associated debt, cyberwar, and cloud computing. (offered in alternate years)

This course presents a systematic introduction to the fundamentals of computational intelligence, including in-depth examination of artificial neural networks, evolutionary computing, swarm intelligence, and fuzzy systems. Computational intelligence is the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. Specific environments examined will include Laboratory Automation, Automated Process Control, Robotics, and Business Decision Support.