Module 7 Overview#
Theme#
Generative models and applications
Essential Question#
What distinguishes discriminative training from generative modeling?
Professional Scenario#
A team is considering generated text, images, or synthetic records and needs to distinguish useful generation from unsupported claims.
Learning Outcomes#
By the end of this module, students will be able to:
explain the core technical idea in precise neural-network vocabulary
connect architecture and training choices to the shape of the data and task
run or interpret the module lab as reproducible evidence
identify limitations, failure modes, and next steps for a defensible experiment
Module Components#
Book prose for conceptual framing and practitioner patterns.
Assignment notebook for the graded artifact: discriminative versus generative comparison.
Slides and narration for structured teaching.
Instructor notes for facilitation, misconceptions, and evidence expectations.
Lab notebook for hands-on experimentation.
Rubric for grading technical correctness, evidence, tradeoffs, risk analysis, and communication.