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