🧠 AI for Students

machine learning · neural networks · training demo · AI types explained

Inside a Neural Network
🎯 Dot Classifier

Click the canvas to place dots. Then train the AI to classify them.

Place at least 3 dots of each class, then click Train AI.

This uses k-Nearest Neighbours — the AI classifies any new point by looking at the k=3 closest training points and taking a majority vote.
What is AI?

Artificial Intelligence (AI) is the field of building computer systems that can perform tasks that normally require human intelligence — like recognising images, understanding speech, playing games, or translating languages. Machine Learning is the most important subfield of AI today.

Why does it matter?

AI is transforming every industry — healthcare (diagnosing diseases), transport (self-driving cars), education (personalised tutoring), and science (predicting protein structures). Understanding the basics of how AI works is an essential 21st-century skill.

Key terms
Training — the process of showing a model labelled examples so it can learn patterns Model — the mathematical function that maps inputs to predictions after training Overfitting — when a model learns the training data too well and fails on new data Gradient descent — the optimisation algorithm that nudges model weights to reduce prediction error Parameters — the numbers inside a model (weights + biases) adjusted during training; GPT-4 has ~1.8 trillion
🎯 Try this challenge

In the Training Demo, place 5 blue dots in one corner and 5 pink dots in another — you'll get 100% accuracy. Now place dots randomly mixed together. Does accuracy drop? This shows why "linearly separable" data is easier for simple classifiers. Also explore what happens with k=1 vs k=5 neighbours in the description.

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