Week 3: Supervised Learning: Teaching with Labels
Dates: Jan 26-30 · Reading: Handout 3: Supervised Learning Basics
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.
Quiz / This Week
Quiz 3. Supervised learning; labels; train/test split; classification intuition.