This course provides an overview of the field of artificial intelligence including history, ethics, data, and key algorithms. It provides demonstrations of key areas in AI that will be explored in depth in later courses.

This course is a continuation of IDAI-101. It provides a continued overview of the field of artificial intelligence but focuses on the specialized algorithms and techniques that are used in different sub areas of the field, including Natural Language Processing, Computer Vision, and Visual Analytics.

Independent study offers a student an opportunity to explore, in greater depth, a topic previously introduced in a prior course or a topic of special interest. A faculty member supervises the student's work. A student and faculty member will collaboratively develop an independent study proposal that describes the student's plan of work, expected deliverables, evaluation criteria, and number of credits that will be earned. Requires department consent.

This course provides students with a foundational understanding of the role of a system administrator in a large organization. Students will configure services, write scripts, and manage Linux and Windows servers. Emphasis is placed on security, deployment strategies, and troubleshooting using modern tools. To support professional growth, students will also develop AI literacy, learning how to evaluate, apply, and critique Artificial Intelligence responsibly and ethically in the context of systems administration. Coursework includes labs, an essay, and a final hands-on practical exam. Together these activities emphasize both technical proficiency and ethical decision-making through applied, project-based learning.

This course introduces the principles and practices of virtualization and cloud computing. Students will examine host-based virtualization platforms and server-side virtualization technologies that enable the consolidation and management of computing resources. The course also explores concepts of public cloud computing, including service models and deployment strategies. Emphasis is placed on understanding the role of virtualization and cloud technologies in modern IT environments, evaluating trade-offs, and applying these tools responsibly and ethically, including the use of Artificial Intelligence (AI) for automation and troubleshooting.

This course introduces configuration management principles with Terraform and Ansible, focusing on automating infrastructure provisioning, repeatable system configuration, and integration strategies grounded in Infrastructure as Code. Students will practice troubleshooting and security while also learning how to apply AI—at a vendor-neutral level—to support configuration drift detection, compliance checks, change-impact analysis, IaC optimization, and self-healing rollbacks. Throughout, we emphasize responsible adoption of AI aligned with the NIST AI Risk Management Framework. Coursework includes hands-on labs, exams, and a team project that brings these skills together in a realistic operations scenario.

This course explores the skills required of a systems administrator in large enterprise environments, focusing on Linux and Windows integration, identity management, monitoring, and centralized logging. Students examine core protocols (DNS, DHCP, SNMP, LDAP, IMAP, SMTP), containerization, and virtualization, along with security practices (TLS, PAM, SELinux, Kerberos, and Group Policy). Emphasis is placed on developing professional writing skills and on AI literacy, including the ethical and responsible use of AI tools for troubleshooting, automation, and documentation.

This hands-on course examines DevOps from an operational perspective, uniting software development and IT operations. You’ll deploy containerized applications, build CI/CD pipelines, orchestrate services, and implement Infrastructure as Code and workflow automation. We also introduce AI in practical, vendor-neutral ways—showing how modern tools can streamline coding and reviews, enhance monitoring and incident response, and improve documentation and knowledge sharing—while emphasizing responsible use and clear guardrails. Along the way, you’ll analyze essential DevOps toolchains and practice version control, automation, and observability on cloud platforms. By the end, you’ll be able to automate, observe, and govern production systems with confidence, pairing core DevOps skills with AI literacy that helps teams ship faster and operate more reliably.