Authoritative Readings and Resources#
These resources extend the supplied learning chapters in AINS6001 Foundations of Artificial Intelligence. They were selected because they are primary standards, official documentation, open textbooks, or authoritative institutional guidance—not unsourced link lists.
How to Read Them#
For each module, read the supplied chapter first. Then use the two linked resources at the end of that chapter to test terminology, compare the course’s worked example with an authoritative treatment, and identify one point that should change or qualify your recommendation. Students are not expected to read every linked document cover to cover.
1. Artificial Intelligence: A Modern Approach#
Foundational AI paradigms, agents, search, reasoning, and learning.
Use with: AI paradigms and system boundaries, Machine learning as empirical inference, Evaluation, uncertainty, and error analysis, Integrated AI system proposal.
2. NIST AI Risk Management Framework#
Operational vocabulary for trustworthy AI design and review.
Use with: AI paradigms and system boundaries, Search, planning, and problem formulation, Evaluation, uncertainty, and error analysis, Human-AI interaction and workflow design.
3. OECD AI Principles#
International principles for responsible stewardship of AI.
Use with: Search, planning, and problem formulation, Knowledge representation and reasoning, Human-AI interaction and workflow design, Responsible AI and governance basics.
4. Stanford AI Index#
Evidence about AI capability, adoption, investment, and impact.
Use with: Knowledge representation and reasoning, Machine learning as empirical inference, Responsible AI and governance basics, Integrated AI system proposal.
Source-Use Standard#
Assignments should distinguish among measured notebook evidence, course-provided synthetic evidence, claims supported by these sources, and the student’s own professional judgment. Cite the specific page, section, control, or documentation topic used; a bare homepage link is not adequate evidence.