Syllabus: AINS6007 Applied AI Programming with Python

Catalog Description

Develops maintainable Python AI applications with testing, APIs, interfaces, packaging, and deployment.

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	Python project structure for AI	What makes AI code maintainable beyond a notebook?	Lab notebook + assignment brief
2	Data handling with Python libraries	How do Python tools support reproducible data work?	Lab notebook + assignment brief
3	Model APIs and reusable components	How should model code expose clear interfaces?	Lab notebook + assignment brief
4	Testing and continuous integration	How do we catch AI application failures early?	Lab notebook + assignment brief
5	Application backends and model services	How does model logic become a service?	Lab notebook + assignment brief
6	User interfaces and workflow integration	How do users interact with AI outputs in context?	Lab notebook + assignment brief
7	Packaging, environments, and deployment	How do environments stay reproducible across machines?	Lab notebook + assignment brief
8	End-to-end AI application	What evidence shows the application works as a system?	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.