Week 2: Data, the Fuel of AI
Dates: Jan 19-23 · Reading: Handout 2: Introduction to Data and AI
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.
Quiz / This Week
Quiz 2. AI versus ML versus rules; features and labels; security data sources.