Week 2: Data, the Fuel of AI

Dates: Jan 19-23 · Reading: Handout 2: Introduction to Data and AI

Lecture Slides

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

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

Learning Objectives

Define artificial intelligence and machine learning in plain language
Distinguish AI, ML, and traditional rule-based software
Identify common sources of security data (logs, emails, network traffic)
Read a dataset in terms of rows, features, and labels

Monday Session

What AI actually is, minus the hype. Rules versus learning from data. Everyday AI in business: recommendations, fraud alerts, spam filters. Where security data comes from: system logs, email, network traffic, and user activity.

Wednesday Session

Anatomy of a dataset: rows, columns, features, and labels. Data quality and why garbage in, garbage out matters for security tools. Visualizing data to spot patterns. Demo: exploring a real phishing email dataset.

Lab

Lab 2: Exploring Security Data. Use pandas in Colab to open a phishing email dataset, count phishing versus legitimate messages, and chart suspicious words and link counts.

⬇ Lab 2 starter notebook (.ipynb)

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

Quiz / This Week

Quiz 2. AI versus ML versus rules; features and labels; security data sources.

In-Class Activity

Data Exposure and Privacy Violations — 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.