# Syllabus: AINS6001 Foundations of Artificial Intelligence

## Catalog Description

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

## Course Structure

Each week includes readings, a lecture/slide sequence, an executable lab, and an applied deliverable. Students maintain a reproducible project record and submit work through the LMS or GitHub workflow selected by the instructor.

## Weekly Schedule

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

## Assessment

| Component | Weight |
|-----------|--------|
| Weekly labs and notebooks | 30% |
| Applied assignments | 35% |
| Participation and technical critique | 15% |
| Final synthesis portfolio | 20% |

## Graduate Expectations

Submissions must show technical reasoning, evidence awareness, clear limitations, and responsible use of AI assistance. Code and analysis should be reproducible enough for instructor review.