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Department of Computer Science and Mathematics
CSC460 Artificial Intelligence [3-0, 3 cr.]
This course is a survey of knowledge-based
artificial intelligence. Topics include the history,
definition, philosophical foundations, search
techniques, game playing, propositional logic,
predicate logic, knowledge representation,
planning, and the natural language processing
and agents.
Prerequisite: CSC310 Algorithms and Data
Structures.
CSC480 Social and Professional Issues in
Computing [3-0, 3 cr.]
This course covers the social impact,
implications, and effects, of computers on
society, and the responsibilities of computer
professionals in directing the emerging
technology. Topics include the history of
computing, the legal and ethical responsibilities
of professionals, the risks to the public, Internet
censorship, industrial intelligence gathering,
intellectual property issues and software
copyrights, environmental concerns, medical
and biotechnology ethics, hacking, professional
liability, “malware” and viruses, whistle blowing,
privacy, data security, and universal accessibility.
The course includes literate business writing,
oral presentations, debates, job hunting and
interviewing, professional etiquette, critical
thinking, and peer reviewing.
CSC490 Software Engineering [3-0, 3 cr.]
This course presents the techniques for
developing reliable, and cost-effective, mediumto-large-scale
object-oriented and classical
software. It also involves project development
to implement these techniques. Topics include
the software life-cycle and process models, the
software requirements elicitation, specification,
and validation techniques, the design
techniques for object-oriented and classical
software (architectural, and component, level
design and the basic unified modeling language
diagrams), software testing (black box and
white box testing techniques), unit, integration,
validation, and system testing, as well as the
basic software project management and quality
issues, and the documentation and technical
writing, and the use of CASE tools.
Corequisite: CSC375 Database Management
Systems.
CSC491 Professional Experience [0-3, 1 cr.]
Students participate in a project-based
Professional Experience in the junior year. The
department requires evidence of project-based
work and relevant professional experience.
Co-requisite: CSC490: Software Engineering
CSC495 IT Project Management [3-0, 3 cr.]
This course introduces managing projects
within an organizational context as well
as managing the changes resulting from
introducing or revising information technology
systems. Topics include project lifecycle,
project stakeholders, project management
skills (leading, communicating, negotiating,
influencing, and presenting), change control
(scope, schedule, cost, quality, risk, project
team, and senior management), project
planning (definition, scope, schedule, costs,
quality, resources, and risks), contingency
planning, project reporting and controls
(definition, scope, schedule, costs, quality,
resources, and risks), the role of IT in
organizational change, activities required to
ensure the success of IT projects (training, job
redesign, communication, etc.). Students are
expected to develop detailed project plans,
schedules, and budgets; estimate project
resources; allocate/coordinate resources;
and interface with management. Handson
experience using project management
software.
Co-requisite: CSC490: Software Engineering
CSC498 Topics in Computer Science
[3-0, 3 cr.]
This course covers selected topics in computer
science. This course may be repeated for credits.
CSC599 Capstone Project [3-0, 3 cr.]
This course is a standalone project course that
allows students to integrate the many concepts,
and skills, they have learned. The course requires
the design, implementation, and oral presentation
of a complete significant team project.
Prerequisite: CSC490 Software Engineering.
GRADUATE COMPUTER SCIENCE
CSC711 Design and Analysis of Algorithms
[3-0, 3 cr.]
This course covers the time and space
complexity of algorithms, the models of
computation techniques for efficient algorithm
design, and the effect of data structure choice
on the efficiency of an algorithm. The course
covers the divide and conquer techniques,
greedy methods, dynamic programming,
amortized analysis, and the graph and network
algorithms, as well as the NP-completeness,
and a selected advanced algorithms.
CSC712 Automata Theory and Formal
Languages [3-0, 3 cr.]
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