Computer Science (DCS)
This course will acknowledge the ways technology can perform unethical types of techniques to users. A smartphone can track or spy as you shop at the grocery store, drones can be used to spy on neighbors and other individuals, algorithms are used in social media to monitor and even track your offline data. Technology was created to benefit and should not cross the line of ethics. Students will analyze the new development and controversies and the short-term and long-term effects of technology use. The aspects of enhanced privacy and security are used in our merging technological society.
Course development in progress
Artificial Intelligence (AI) enthusiast project future where autonomous entities carry out intelligent tasks. Researchers, however, continue to announce the difficulties understanding of the constructs of intelligence. Indeed the Alan Turing Test of Intelligence seems to form a good foundation for understanding the represented constructs of problem solving.
Machine learning (ML) is just one of the many application domains that researchers have claimed to be a significant component of artificial intelligence (AI). The focus of this course is to investigate the ML concepts applicable to most decision-making systems not bound by the constraints of AI. Students will study decision making rules using predictive models (i.e. regression (magnitude models), classification (probability models), scoring (scorecards, decision trees, and neural networks), the constructs of structured and unstructured data, supervise and unsupervised learning, hypothesis creation and testing, as well as anomaly detection and impact. Topics will also include deep learning, reinforced learning, and natural language processing.
Software is becoming ever more important to our daily lives, as well as every sector of industry. As software systems increase in capability, they increase in complexity, resulting in delays, defects, and vulnerabilities. Software Engineering research is applying rigorous scientific approaches to address real and meaningful technical challenges.
In a constantly changing business climate the importance of understanding data, information, and knowledge, both explicit and tacit, remain paramount to business success (as well as personal victories). Based on customer touchpoints, advertisers are now able to promote products shoppers are likely to purchase. The constructs of data, information, and knowledge are not limited to advertiser. Indeed, since the advent of the machine age, businesses have been using data gathering, analysis, and knowledge management systems to understand current inventory levels and manufacturing requirements in order to improve Return on Investment decisions. The focus of this course will encourage student to go beyond the basics of such constructs and promote better decision making using Machine Learning (ML) as well as one dimension of Artificial Intelligence (AI).
Given today s technologically advanced society, the critical need for data collection and analysis, are at odds with societal needs for privacy. Researchers and politicians alike have suggested the likelihood of Cyberwarfare will continue to increase in intensity as we move to the next century. Indeed, it has never become more important for leaders and educators to understand the current body of knowledge, skills, techniques, and tools used to recognize and mitigate cyber-attacks. Students will research current computer and network hardware / software used to identify harmful digital activities and associated actions that can be taken to prevent harmful data breaches.
Computer Science is an ever-changing field. Students in DCS740 will consider current trends, issues and events in the field.
This course provides students with an opportunity to gain practical work experience, linking that experience to the Doctor of Education in Computer Science courses learning outcomes. Students will submit papers providing a job description, resume and correlation of the work experience with courses in which the student is enrolled for the respective term. Students may work any number of hours per week throughout the academic term, must be enrolled in at least two other graduate course leading to their degree, and maintain a 3.0 GPA. A maximum of twelve Experiential Learning courses can be taken; however, only one can be taken in any academic term. There are no graduate credit hours for this course.