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.