Lesson 4: Neural Networks for Perceptual Rules

Micro-Topic 4.1: Why Neural Networks for Perception?

Goal: Motivate why we need neural networks by discussing tasks like vision and audio that involve patterns too complex for manual rules or simple trees, and how neural networks mimic a brain-like approach to handle these.

Micro-Topic 4.2: What is a Neural Network?

Goal: Provide a conceptual understanding of artificial neural networks – nodes (neurons) connected by weights, organized in layers. Keep it simple: focus on the idea of inputs going through hidden layers to produce outputs, and that the network learns by adjusting weights.

Micro-Topic 4.3: Learning and Training Neural Networks

Goal: Dive a bit more into how neural networks learn from data – the concept of training with many examples, the role of a loss function and optimization (gradient descent), and the need for lots of data. Keep it conceptual (no heavy math) but ensure students know it's a trial-and-error weight adjustment process.

Micro-Topic 4.4: Example – Recognizing Handwritten Digits (NN vs. Decision Tree)

Goal: Compare a neural network’s approach to a decision tree’s approach on a concrete problem: identifying handwritten digits (0-9) from images. This highlights how neural nets handle such perceptual tasks better, and what their results look like.

Micro-Topic 4.5: Neural Networks vs. Decision Trees – Key Differences

Goal: Summarize the differences between neural networks and decision trees in terms of how they work, what they’re good at, interpretability, data requirements, etc., reinforcing why we introduced neural nets after trees for different kinds of problems.