Week 1: Introduction

Learning Objectives

By the end of this week, students will be able to:

• Define machine learning and distinguish it from traditional programming
• Identify the main categories of ML (supervised, unsupervised, reinforcement learning)
• Describe the general ML workflow: problem framing, data, model, evaluation
• Set up a Python/Jupyter environment for the course

Perspectival Reading

Reading: TBD — e.g., Chapter 1 of course text

Reflection Questions

1. The author frames machine learning as a fundamentally statistical enterprise. What does that mean for how we interpret model outputs?
2. What assumptions are embedded in the idea that past data can predict future outcomes?
3. Who gets to decide what problems machine learning should be applied to?

Slides

View slides

Notebook Demo

Open in Google Colab (link TBD)

Lab Assignment

Week 1 Lab — GitHub Classroom (link TBD)