Week 3: Supervised Learning: Teaching with Labels

Dates: Jan 26-30 · Reading: Handout 3: Supervised Learning Basics

Lecture Slides

⬇ Monday slides (PPTX) ⬇ Wednesday slides (PPTX)

Full 75-minute decks, CSU-branded, with detailed speaker notes on every slide.

Learning Objectives

Explain supervised learning as learning from labeled examples
Describe the difference between training data and testing data
Walk through how a classifier decides between classes
Connect classification to spam and phishing detection

Monday Session

Supervised learning intuition: showing a model thousands of labeled examples so it can label new ones. Classification explained with the spam filter story. Training versus testing and why we never grade a model on questions it has already seen.

Wednesday Session

A visual tour of two beginner-friendly classifiers: decision trees and k-nearest neighbors. Live demo: training a spam classifier step by step in Colab.

Lab

Lab 3: Build Your First Spam Classifier. Train a decision tree to classify emails, split data into train/test sets, and test it on new emails.

⬇ Lab 3 starter notebook (.ipynb)

Download, then open in Google Colab (File → Upload notebook). Submit your completed notebook in Canvas.

Quiz / This Week

Quiz 3. Supervised learning; labels; train/test split; classification intuition.

In-Class Activity

Phishing and Social Engineering — Hacker101-style guided security lesson. Graded, 20 points.

⬇ Activity slides (PPTX) 📄 Student worksheet (PDF)

Graded in-class activity with a worksheet. Full rotation on the Activities page.

← All lecture notes · Detailed slides and notes are filled in with the lecture-builder skill.