Courses

This course encourages students to explore in-depth artistic and technical challenges of designing believable, expressive 3D characters. Emphasis is placed on understanding form, structure, and surface through sculpting, with special attention to anatomy, gesture, and stylization. Students will build on prior experience to develop characters that feel both dimensional and intentional- balancing realism with imagination.

This course builds upon 3D Animation I, emphasizing the foundations of more advanced animation techniques, collaborative workflows, and cinematic storytelling. The course will focus on refining animation quality, achieving realism and expressiveness, and optimizing production efficiency. It will cover topics such as non-linear animation, physics-based animation, and real-time rendering.

This course will provide candidate with an overview of the philosophy of behaviorism and the science of behavior analysis. The focus will be practical skills to plan, implement, and evaluate behavior management programs for diverse learners. In addition, ethical considerations regarding behavior change procedures will be described.

Provides a working understanding of various disorders treated and managed by professional psychologists. Includes familiarity with operationalized diagnostic criteria and theoretical perspectives on various disorders. Usually offered Spring term.

A study of accounting applications, cyber security, and privacy concerns when evaluating accounting systems, processes, and internal procedures. This course applies concepts using technology.

Development of adapted programs to meet the needs of children with exceptionalities. Identification and diagnosis of various discrepancies also included. May be taught concurrently with PHED 4160.

Scientific principles applied in aerospace education. Development of model rockets and aircraft. Preparation of advanced materials for teaching.

A study of advanced AI theory and implementation. Topics include neural networks, probability learning, and a variety of related topics. A programming language (LISP or R) will be utilized to solve complex industry problems associated with AI applications.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, and support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control.

To progress to this course, all previous coursework counting towards the graduate or doctoral nursing degree must be completed with a grade of "B" or better. The purpose of this course is to refine management strategies and best practice models in the delivery of high quality care to late adolescent, adult, and geriatric clients. The focus of this course is to evaluate progress toward the achievement of professional competencies.