Cognitive Science - UNC Charlotte

Graduate Certificate Program

The Cognitive Science Certificate Program involves 15 hours of coursework. Students must take the required introductory course and at least two of the disciplinary courses. The remaining hours may come from any of the other topics courses listed or from the disciplinary courses. A cumulative GPA of 3.0 will be required and at most one course with a grade of C may be allowed toward the certificate.

Admission requirements

The certificate program is open to all students who hold a bachelor's degree from an accredited university and either

1. are enrolled and in good standing in a graduate degree program at UNC Charlotte, or

2. have a minimum GPA of 3.0 for their undergraduate courses.

Application for the Cognitive Science Certificate Program is made through the Office of Graduate Admissions.


PSYC/ITCS/ITIS 6216 Introduction to Cognitive Science

Disciplinary courses

Must take at least two of the following.

PSYC 6116 Cognition

ENGL 6163 Language Acquisition

PHIL 6630 Philosophy of Mind

ITCS 6150 Intelligent Systems


PSYC 6015 Topics in Perception & Physiological Psychology,

PSYC 5316 Cognitive Neuroscience

PSYC 6115 Sensation and Perception

PSYC 6102/8102 Research Design and Quantitative Methods

ITIS 6400/8400 Principles of Human Computer Interaction

TIS 6510/8510 Software Agent Systems

ITCS 5151 Intelligent Robotics

ITCS 5152 Computer Vision

ITCS 6153 Neural Networks

ITCS 6156 Machine Learning

ITCS 6159/8159 Intelligent Tutoring

ITCS 6170 Logic for AI

ITCS 6158 Natural Language Processing

ITCS/ITIS 6500/8500 Complex Adaptive Systems

ECGR 5196 Introduction to Robotics

ECGR 6102 Optimization of Engineering Designs

ECGR 6266/ ECGR 8266Neural Networks Theory and Design

Course Descriptions

CEGR 5181 Human Factors in Traffic Engineering
Topics, seminars, or other courses in the cognitive sciences approved by the Program Coordinator.

CEGR 5181 Human Factors in Traffic Engineering. (3)
Study of the driver's and pedestrian's relationship with the traffic system, including roadway, vehicle and environment. Consideration of the driving task, driver and pedestrian characteristics, performance and limitations with regard to traffic facility design and operation.

ECGR 5196. Introduction To Robotics. (3)
Prerequisites: ECGR 2103 or MEGR 2101 and senior standing. Modeling of industrial robots including homogeneous transformations, kinematics, velocities, static forces, dynamics, computer animation of dynamic models, motion trajectory planning, and introduction to vision, sensors and actuators (dual-listed with MEGR 4127). (Fall)

ECGR 6102. Optimization of Engineering Designs. (3)
Prerequisite: ECGR 5101 or consent of department. The development of computationally feasible algorithms for solving optimization problems in engineering designs. Introduction to non-linear programming methods; study of constrained and unconstrained problems, linear programming problems and other related topics. (On demand)

ECGR 6266/ 8266 Neural Networks Theory and Design. (3)
Topics include: Neural network model and network architectures; single layers, multiple layers network, perceptron learning rules; supervised hebian learning; performance optimization; widrow hoff learning; backpropagation; associative learning; competitive learning; grossberg network; Hopfield network; application of neural network. (On demand)

ENGL 5263. Linguistics and Language Learning. (3)
Readings in, discussions of, and application of linguistically oriented theories of language acquisition, directed toward gaining an understanding of language-learning processes and stages. (Yearly)

ENGL 6070. Topics in English. (3)
Selected topics of literature and language. May be repeated for credit as topics vary and with English Department approval. (Fall, Spring)

ITCS 5151. Intelligent Robotics. (3)
Prerequisites: ITCS 1215 and MATH 2164, or consent of the Department. General introduction to spatial descriptions and transformations, and manipulator position and motion. More study on robot planning, programming, sensing, vision, and CAD/CAM. (Odd, spring) (Evenings)

ITCS 5152. Computer Vision. (3)
Prerequisites: ITCS 1215 or MATH 2164, or consent of the Department. General introduction to Computer Vision and its application. Topics include low level vision, 2D and 3D segmentation, 2D description, 2D recognition, 3D description and model-based recognition, and interpretation. (Odd, Spring) (Evenings)

ITCS 6153. Neural Networks. (3)
Prerequisites: ITCS 6114. Topics include: Basic notions and models of artificial neural nets; single layer neural classifiers; multilayer one-way neural nets; single layer feedback networks; neural models of associative memory; self organizing neural nets; translation between neural networks and knowledge bases; applications of neural networks. (Even, Fall) (Evenings)

ITCS 6156. Machine Learning. (3)
Prerequisite: ITCS 6150 or consent of the department. Machine learning methods and techniques including: acquisition of declarative knowledge; organization of knowledge into new, more effective representations; development of new skills through instruction and practice; and discovery of new facts and theories through observation and experimentation. (On demand)

ITCS 6010. Topics in Computer Science. (3)
Prerequisite: consent of the department. Topics in computer science selected to supplement the regular course offerings. May be repeated for credit as topics vary. (On demand)

ITCS 6170. Logic for Artificial Intelligence. (3)
Prerequisite: ITCS 6150 or consent of the department. Introduction to basic concepts of logic for artificial intelligence, including declarative knowledge, inference, resolution, non-monotonic reasoning, induction, reasoning with uncertain beliefs, distributed information systems, intelligent information systems, planning and intelligent-agent architecture. (On demand)

ITCS 6150. Intelligent Systems. (3)
Prerequisites: full graduate standing or consent of the department. To introduce core ideas in AI. Heuristic versus algorithmic methods; problem solving; game playing and decision making; automatic theorem proving; pattern recognition; adaptive learning; projects to illustrate theoretical concepts. (Fall) (Evenings)

ITCS 6158. Natural Language Processing. (3)
Prerequisite: ITCS 6150. Principles, methodologies, and programming methods of natural language processing including foundations of natural language understanding, namely: lexical, syntactic, and semantic analysis, discourse integration, and pragmatic and morphological analysis. (On demand)

ITCS/ITIS 6500/8500 Complex Adaptive Systems.
Prerequisite: Permission of instructor. Complex adaptive systems (CAS) are networked (agents/part interact with their neighbors and, occasionally, distant agents), nonlinear (the whole is greater than the sum of its parts), adaptive (the system learns to change with its environment), open (new resources are being introduced into the environment), dynamic (the change is a norm), emergent (new, unplanned features of the system get introduced through the interaction of its parts/agents), and self-organizing (the parts organize themselves into a hierarchy of subsystems of various complexity). Ant colonies, networks of neurons, the immune system, the Internet, social institutions, organization of cities, and the global economy are a few examples where the behavior of the whole is much more complex than the behavior of the parts. This course will cover those and similar topics in an interactive manner. Examples of our current research effort will be provided. Topics include: Self-organization; emergent properties; learning; agents; localization affect; adaptive systems; nonlinear behavior; chaos; complexity. (On demand)

PHIL 6630 - Philosophy of Mind (3).
This course addresses questions concerning the relationship between body and mind, the existence of other minds, the nature of consciousness, and the architecture of cognition. Approaches to these questions include traditional philosophical sources (emphasizing metaphysics and epistemology) and more recent developments in cognitive science (including the computational model of mind, mental representation, connectionist systems, and artificial intelligence). Also addressed are ethical and social issues involved in the design and implementation of intelligent systems. Inquiries bear on issue such as free will and determinism, emotion and reasoning, and the nature of rationality. (Alternate Years)

PSYC 5316. Cognitive Neuroscience. (3)
Prerequisite: graduate standing or permission of the instructor. Biological basis of consciousness and the neurobiology of mental processes by which we perceive, act, learn, and remember; representation of mental processes from electrophysiological and brain imaging techniques, clinical neurology, and computational science. (Alternate Years).

PSYC 6015. Topics in Perception and Physiological Psychology. (3)
An examination of selected topics in the areas of sensation and perception, physiological and neuropsychology, with an emphasis on the applications to the areas of clinical, community, and industrial psychology. May be repeated for credit with the permission of department. (Alternate years)

PSYC 6102. Research Design and Quantitative Methods in Psychology. (3)
Prerequisites: MATH 1222 and PSYC 2102 or equivalent. Experimental and correlational methods of psychological research, including single subject designs with emphasis on research design and the application of statistical methods to psychological research. (Fall)

PSYC 6115 Sensation and Perception. (3).
Processes involved in receiving and interpreting sensory data including all the sensory systems with an emphasis on vision. (On Demand)

PSYC 6116 Cognition (3).
Concerned with how humans acquire information, retain information in memory, and use this information to reason and solve problems. Current emphases include memory, category learning, planning, concept formation, problem solving, mental models, and knowledge representation. (Alternate Years)

PSYC 6216/ITCS 6216/ITIS 6216 Introduction to Cognitive Science (3).
This course presents multiple perspectives on the study of intelligent systems. Broad coverage of such topics as philosophy of mind; human memory processes; reasoning and problem solving; artificial intelligence; language processing (human and machine); neural structures and processes; and vision. Also included is participation in the cognitive science seminar (Same as ITCS 6216, and ITIS 6216) (Spring Semester).