# Module 4 Overview

## Theme

Convolutional neural networks for vision

## Essential Question

Why do convolutions, pooling, and translation equivariance matter for image understanding?

## Professional Scenario

A computer-vision team is deciding whether a small CNN is adequate for grayscale inspection images before moving to larger pretrained models.

## 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: CNN architecture memo with shape trace and receptive-field rationale.
- 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.