Week 1: Fundamentals of AI

Tue, 05 May 2026 00:00:00 +0000

Focus: What is AI/ML? Brief history and overview

📚 Required Readings

“Artificial Intelligence: A Brief History” (15 min read)
- Overview of AI development from symbolic AI to modern machine learning
- Key figures and pivotal moments in AI research
“What is Machine Learning?” (20 min read)
- Difference between AI and machine learning
- Supervised vs. unsupervised learning basics
- Real-world examples of ML applications

Stanford Encyclopedia of Philosophy: Artificial Intelligence (link) — Philosophical foundations
“A Very Short Introduction to Artificial Intelligence” — Excerpts (10 min)

Before class, consider these questions as you read:

Tue, 05 May 2026 00:00:00 +0000

Focus: How AI systems work; data and training

“How Machine Learning Works: The Training Process” (25 min)
- Training data, features, and labels
- Algorithms and parameter optimization
- Testing and validation
“Data: The Fuel of AI” (20 min)
- Data collection and preparation
- Data quality and representation
- The role of big data in modern AI

Find a dataset related to a topic you care about (e.g., Kaggle, UCI Machine Learning Repository)
Reflect on what patterns you’d hope an AI system would find in that data
Consider: What could go wrong if the data was biased or incomplete?

See Week 2 Assignment for this week’s task.