# AINS6001: Foundations of Artificial Intelligence

**Aurnova MSAI track:** Core  
**Credits:** 3  
**Format:** 8-week online graduate course

Introduces AI concepts, paradigms, reasoning, evaluation, and responsible system design.

This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.

## Course Outcomes

By the end of the course, students will be able to:

- explain the major concepts and tradeoffs in Foundations of Artificial Intelligence;
- build or evaluate applied AI artifacts aligned with the course domain;
- document assumptions, evidence, limitations, and operational risks;
- connect technical work to governance, stakeholder needs, and deployment readiness.

## Module Map

1. **AI paradigms and system boundaries** — What counts as an AI system, and where do its responsibilities begin and end?
2. **Search, planning, and problem formulation** — How do search and planning convert goals into tractable state spaces?
3. **Knowledge representation and reasoning** — How do formal representations support reliable inference?
4. **Machine learning as empirical inference** — How does learning from data differ from explicit programming?
5. **Evaluation, uncertainty, and error analysis** — How do we know whether an AI system is useful, robust, and honest?
6. **Human-AI interaction and workflow design** — How should AI support decisions without hiding accountability?
7. **Responsible AI and governance basics** — What risks must be addressed before deployment?
8. **Integrated AI system proposal** — How do technical, human, and governance choices fit into one defendable plan?