Module 1 Overview#
Theme#
From neurons to multilayer networks
Essential Question#
How does a stack of differentiable units approximate complex functions from data?
Professional Scenario#
A product analytics team has a small labeled dataset and wants a neural baseline before investing in a larger modeling effort.
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: baseline MLP design brief.
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.