AI & Machine Learning

Students don't just use AI — they understand how it works, build their own models, and think critically about its impact on the world.

90 min per session

2× per week, 6 weeks

Max 8 students

12 total sessions

What Makes This Course Different

Hands-On, Not Lecture

Students train real AI models using tools like Google's Teachable Machine and ML for Kids + Scratch.

Ethics Built In

Every unit asks: should we? as much as can we? Critical thinking is core, not an afterthought.

Generative AI Included

Students explore how ChatGPT and image generators actually work — and learn to use them critically, not blindly.

Full Curriculum — Week by Week

Unit 1Week 1 · Sessions 1–2

What is AI?

Topics

AI vs Machine Learning — what's the difference?
AI in everyday life (TikTok, Netflix, Spotify, self-driving cars)
Narrow AI vs General AI
Why data is the fuel that powers AI

Activities

"Is this AI?" classification game
Train a first image classifier using Google's Teachable Machine

Students walk away with a clear mental model: AI = patterns learned from data, not magic.

Unit 2Week 2 · Sessions 3–4

How Machines Learn - Using Edge Impulse

Topics

Training data vs testing data
What a classification model actually does
Accuracy, confidence scores, and bias
Why bad data = bad AI

Activities

Learn to use Edge impulse to capture and train data
Build a rock-paper-scissors hand gesture classifier(image classification)
Audio classification with key words

Students can train and evaluate a model and explain why garbage in = garbage out.

Unit 3Week 3 · Sessions 5–6

Types of AI

Topics

Supervised learning — learn from labeled examples
Unsupervised learning — find hidden patterns
Reinforcement learning — learn by trial and error

Activities

Human sorting game to simulate clustering
Watch a game AI teach itself to play — then discuss how it learned

Students can match a real-world problem to the right type of ML approach.

Unit 4Week 4 · Sessions 7–8

Neural Networks & Generative AI

Topics

What is a neural network? Inputs → hidden layers → outputs
How ChatGPT and image generators actually work
Why these models are powerful — and why they make things up

Activities

Human neural network simulation — students physically act as neurons
Explore and interrogate a generative AI tool: ask it hard questions, try to break it

Students understand the intuition behind modern AI — and see it as an impressive but imperfect tool, not a magic oracle.

Unit 5Week 5 · Sessions 9–10

AI Ethics & Society

Topics

Bias in AI — who gets hurt when data is skewed?
Privacy, surveillance, and data collection
AI in hiring, college admissions, and medicine
Who is responsible when AI gets it wrong?

Activities

Structured debate: Should AI decide college admissions?
Case study deep-dive on a real AI failure — discuss what went wrong and who was harmed

Students think critically about who benefits, who gets hurt, and what questions we should demand answers to.

ShowcaseWeek 6 · Sessions 11–12

Final Project Showcase

Topics

Define a real problem worth solving
Collect and curate training data
Train a model and evaluate its performance
Present results honestly — including what it gets wrong

Activities

Session 11: Build — image classifier, voice recognition model, or AI-powered Scratch game
Session 12: Live showcase — demo the project, explain how it works, discuss its limitations

Students present a working AI project to the group, explain the choices they made, and reflect on what they'd improve.

Ready to enroll in AI & ML?

Only 8 spots per cohort. Schedule TBD — sign up and we'll confirm session times.