All 18 graduate Computer Science courses
Filter by level, by how central AI becomes after revision, by whether the course belongs to the MSAI proposal or is shared with the MSCS program, or by the program learning outcome you need evidence for.
AI Ethics and Vocation
Agentic AI shifts the ethical question from what a model outputs to what a system does on someone's behalf, so a practitioner's accountability now extends to delegated action, machine-produced deliverables, and the data and labor supply chain behind a model. The course keeps its vocation-grounded, case-study identity and adds delegated agency - escalation, guardrails, audit, provenance, and disclosure - as the setting in which that moral reasoning is practiced.
Applied Artificial Intelligence
Applied AI is no longer a survey of separate perception techniques: practitioners now assemble vision, speech, and language capability out of pretrained and hosted models, wire them together with tool-calling agents and retrieval, and are judged on evaluation, cost, and accountability rather than on hand-built pipelines. The course therefore moves from demonstrating techniques to specifying, orchestrating, evaluating, and taking responsibility for working AI systems.
Data Security and Information Assurance
Enterprise defense now has to cover systems that are themselves built and operated by AI: prompt injection, tool and model supply-chain compromise, over-scoped agent credentials, and machine-generated code and runbooks reaching production are ordinary incident causes, while detection authoring, log correlation, and triage are increasingly AI-assisted. The course therefore treats the model, retrieval, and agent-tool layer as a first-class attack surface and holds students accountable for evaluating and bounding machine-produced security work.
Mobile Computer Architecture
On-device inference has made neural accelerator provisioning, model quantization, memory bandwidth, and energy per inference first-order mobile architecture constraints rather than specialist concerns, and AI coding agents now produce much of the profiling and kernel-level optimization work architects once wrote by hand. The course therefore budgets silicon for inference workloads and requires agent-produced analysis to be verified against measured device telemetry before it informs a design decision.
Applied Computer Networking
Network engineering and operations are now largely machine-assisted: telemetry is triaged by learned detectors, configuration and remediation are drafted by AI agents, and the enterprise network has become the delivery and control path for AI services. The course keeps its networking identity and adds the evaluation, validation, and accountability skills a practitioner needs to supervise that automation, including the rollout and rollback discipline a machine-proposed change must clear before production.
Database Administration
Database administration has become a control point for AI systems: production stores now hold the embeddings and governed records that retrieval-augmented applications and model training depend on, while schemas, queries, and migrations are increasingly drafted by AI coding agents and executed by automated service accounts. The course therefore adds vector and hybrid indexing, benchmarking of AI-assisted and learned query optimization, provenance for training data, and least-privilege access and auditing for agent traffic, without displacing core relational design and operations.
Advanced Algorithms
Advanced algorithm work has changed in two ways that reach this course: implementations are now routinely drafted by AI coding agents, so empirical verification of machine-produced code and its asserted complexity bounds becomes an assessed skill rather than an assumed one, and AI infrastructure has become one significant consumer of advanced algorithms, alongside scheduling, networks, and large-scale data processing, through vector and nearest neighbor search, sketching and streaming, and heuristic search. The course therefore keeps its design, analysis, and intractability core and adds those developments as application context and as a verification discipline rather than as its organizing frame.
Applied Restful APIs and Integrations
In 2026 an API is no longer consumed only by human-written client code: it is the surface through which tool-calling LLM agents obtain real-time knowledge and take action, so contract design, authorization scope, idempotency and observability now carry safety weight they did not previously carry. The course keeps its RESTful and integration core while adding function-calling and Model Context Protocol tool surfaces, prompt-injection and tool supply-chain risk, agent evaluation harnesses, and cost and latency budgets for agent-orchestrated workflows.
Language Theory
Production code is increasingly written by AI coding agents, so the professional value of language design has shifted from developer ergonomics alone toward machine-facing guarantees: grammars and schemas that constrain what a model may emit, type and effect systems that make generated code checkable before it runs, and harnesses that measure where generated programs fail. The course accordingly treats syntax and semantics as the enforcement layer for agent-produced software rather than as purely human-facing notation.
Industrial AI Application and Practice
Industrial AI deployment in 2026 is no longer only a matter of shipping a trained model: enterprises increasingly ship tool-calling agents that read live systems, act on them, and are themselves built with AI coding agents, so the professional work shifts to specification, guardrails, evaluation harnesses, observability, and accountability for machine-produced output. The course therefore keeps its MLOps and enterprise-deployment spine and extends it to agentic architecture, retrieval over enterprise data, cost and latency budgets, human-in-the-loop escalation, and post-deployment drift monitoring.
Data Acquisition and Management
Agentic systems have made data infrastructure the substrate of AI behavior: retrieval corpora, embeddings, permission-aware access and provenance now determine what an agent can know, do and be held accountable for, and ingested content has become an attack surface through poisoning and indirect prompt injection. The course therefore extends classic acquisition, ETL and governance work to agent-facing retrieval layers, dataset lineage and licensing, and the evaluation of pipelines and pipeline code produced with AI coding agents.
Neural Networks and Deep Learning
Deep learning practice in industry has shifted from training models from scratch toward adapting, compressing, and serving pretrained foundation models as components that agentic systems call, with practitioners accountable for evaluating machine-generated training code and the behavior of the resulting models. The course therefore keeps its mathematical and architectural core but adds parameter-efficient adaptation, reproducible evaluation harnesses, provenance and drift monitoring, and packaging a trained model as a callable, documented tool.
Natural Language Processing
Natural language processing has become the substrate of agentic AI: the professional work has shifted from training task-specific models toward specifying, grounding, orchestrating, and evaluating language models that call tools and act on retrieved knowledge. The course therefore keeps its linguistic and neural foundations while adding retrieval-augmented generation, tool calling and the Model Context Protocol, injection guardrails, and evaluation harnesses with LLM-as-judge scoring and cost and latency budgets.
Applied Research Practicum
In a practicum that mirrors 2026 professional practice, the student's contribution has shifted from writing most of the code to specifying, orchestrating, reviewing, and evaluating work produced by AI coding agents and tool-calling language models. The course therefore holds students accountable for machine-produced deliverables — their verification, their disclosure to the sponsor, and their behavior after deployment — rather than only for their own keystrokes.
System Administration and Maintenance
Administration is now performed both with AI and for AI: triage, runbooks, and configuration are increasingly drafted by agents holding privileged tool access, while the fleet must also carry model-serving stacks under accelerator, cost, and latency budgets. The course keeps its operating-system and administrative-domain core as graded operational work and adds telemetry analytics, measured evaluation of AI-assisted operations, and accountability for machine-initiated changes.
Advanced Networking
Network operations are now data- and agent-mediated: telemetry is triaged by machine-learning detectors, configuration and remediation are drafted by AI tooling that must be verified before it reaches production, and AI workloads themselves have become a dominant traffic class shaping fabric design. The course keeps its networking identity and adds the measurement, detector-evaluation, and control-plane accountability skills that shift demands.
System Analysis and Design
Agents and large language models now produce the analyst's traditional deliverables - user stories, process and data models, prototypes - so professional value in systems analysis has shifted toward elicitation quality, verification of machine-drafted artifacts against real stakeholders, and specifying systems whose components behave probabilistically. The course therefore adds verification of agent-drafted requirements, acceptance criteria and monitoring for non-deterministic components, accountability and escalation as specified requirements, and evidence-based evaluation of vendor AI claims, without displacing the course's requirements, architecture, modeling, and project commitments.
Internship in IT
Interns now enter IT workplaces where AI coding assistants and tool-calling agents are increasingly ordinary tooling, so the professional expectation has shifted from producing work by hand to specifying, reviewing, verifying, and taking accountability for machine-produced work under employer data-handling and disclosure expectations. The internship therefore adds explicit supervision of how the student uses those tools on site and how their own contribution is documented, without displacing the placement's core purpose of integrating coursework with supervised practice.
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