500
Overview of computer organization, programming languages, various computer applications, information management systems and ethical implications of computing. Does not count toward CPS major or minor or the MS in Computer Science. Prerequisite: Senior or graduate standing.
Credits
3(3-0)
Prerequisites
Senior or graduate standing
Corequisites
None.
Overview of tools and techniques to process text data with the aim of production actionable insights. Introduction to the cleaning, filtering, transforming and querying of text data. Prerequisites:
CPS 181,
CPS 240 or
CPS 280 or
CPS 285; or admitted to the Master of Science in Computer Science program.
Credits
3(3-0)
Prerequisites
CPS 181, CPS 240 or CPS 280 or CPS 285; or admitted to the Master of Science in Computer Science program
Corequisites
None.
Study of design and implementation issues in distributed programming, including event model, client/server, distributed data processing, remote objects, network services, and security. Prerequisite:
CPS 340 or admission into CPS graduate program.
Credits
3(3-0)
Prerequisites
CPS 340 or admission into CPS graduate program
Corequisites
None.
Introduction to modern large language models including their architecture, training, applications and implications. Exploration of their capabilities, limitations, and ethical and societal impacts. Prerequisite: CPS 480 or CPS 580 or admitted to the Master of Science in Computer Science program.
Credits
3(3-0)
Relational databases, data modeling, NoSQL databases, and data warehousing. Design and develop databases for various applications. Prerequisites:
CPS 340 or
ITC 341 or graduate standing.
Credits
3(3-0)
Prerequisites
CPS 340 or ITC 341 or graduate standing
Corequisites
None.
Analysis of algorithms including computing time and space requirements. Efficient algorithm design techniques. Introduction to parallel algorithms and computational complexity classes. Prerequisite:
CPS 340 or admitted to the Master of Science in Computer Science program.
Credits
3(3-0)
Prerequisites
CPS 340 or admitted to the Master of Science in Computer Science program.
Corequisites
None.
Theory and tools for supervised machine learning. Overview and application of architectures and training techniques applicable to image classification, object recognition and text generation. Prerequisites:
CPS 340 or
ITC 510 or
CPS 285,
DAS 450.
Credits
3(3-0)
Prerequisites
CPS 340 or ITC 510 or CPS 285, DAS 450
Corequisites
None.
Use of elementary automata theory, limits on computation, cellular automata, neuron models, evolutionary (genetic) algorithms and learning, and Artificial Immune Systems, DNA, and quantum computing. Prerequisites:
CPS 340 or graduate standing.
Credits
3(3-0)
Prerequisites
CPS 340 or graduate standing
Corequisites
None.
This course covers practical innovations in computer vision, focusing on image enhancement, feature extraction, and deep learning techniques for real-world applications. Prerequisite: CPS 280 or CPS 580 or graduate standing.
Credits
3(3-0)
Elementary nonlinear dynamics and chaos theory. Correlation dimension. Approximate, Sample, and Multiscale entropies. Cellular automaton dynamics. Entropy analysis and learning in time series data. Prerequisites:
CPS 340 or graduate standing.
Credits
3(3-0)
Prerequisites
CPS 340 or graduate standing
Corequisites
None.
Study of data engineering concepts and techniques, including data collection, cleaning, transformation, security and storage. Construction of a data pipeline in an applied setting. Prerequisites: CPS 181; CPS 240 or CPS 280 or CPS 285; or admitted to the Master of Science in Computer Science program. Recommended: ITC 341.
Credits
3(3-0)
Prerequisites
CPS 181; CPS 240 or CPS 280 or CPS 285; or admitted to the Master of Science in Computer Science program
Corequisites
None.
Overview of the implications and application of artificial intelligence to a developed computing solution. Culminating experience for minor in Applied Artificial Intelligence. Pre/Co-requisites: CPS 480, CPS 580.
Credits
2(2-0)
Selected topics in computer science not otherwise available in a course regularly offered. Topic(s) are listed in Course Search and Registration. Repeatable up to 6 credits when content does not duplicate previous special topic studies. Prerequisite: See Course Search and Registration. Determined by the specific topics presented.
Credits
1-6(Spec)
Exploration of topics in Computer Science agreed upon by student and instructor. May be taken for credit more than once, total credit not to exceed six hours. Prerequisite: Permission of instructor.
Credits
1-6(Spec)