Module 3 Overview

Module 3 Overview#

Theme#

Optimization, loss, and regularization

Essential Question#

How do we train deep networks reliably when loss surfaces are non-convex and data are noisy?

Professional Scenario#

A modeling team has a prototype that trains inconsistently and needs a defensible training configuration before scaling experiments.

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: optimizer comparison memo using loss curves, accuracy, and regularization tradeoffs.

  • 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.