Module Check
You have covered a lot of ground. You started with the question of why hand-coded rules were not enough, moved through the three kinds of learning, built an understanding of what a model is and how it trains, explored ML in your daily life, and examined where learning fails. This lesson pulls it all together — not to repeat what you already know, but to make sure the ideas are connected and ready to be built on in the next module.
Key Terms Review
Flashcards — click each card to reveal the answer
Module Quiz
A programmer writes: if temperature > 100 then alert = true. A data scientist trains a model on patient records to predict fever. Which is machine learning and why?
During training a model predicts cat for an image of a dog. The loss is measured as large. What happens next in the training loop?
A music app clusters listeners into 12 taste groups without being told what those groups should be. Which learning type is this, and what is the giveaway?
Which statement about ML models is FALSE?
A new disease emerges that no doctor has ever documented. Why would a machine learning diagnostic tool be unreliable for this disease?
You discover an app whose text suggestions change and improve as you use it over months, adapting to your writing style. Is this most likely ML or rule-based?
Machine learning does not replace programming — it changes what programming is for. When the rules are too many, too complex, or too tacit for a human to write, you give the system data and let it find the rules. The result is a model: a frozen artifact of learned patterns ready to make predictions. That model has real power and real limits, and knowing both makes you a smarter builder and user of AI.
Capstone — Design a Learning System
- Step 1: Choose a real problem you care about. It should be complex enough that writing all the rules by hand would be impractical.
- Step 2: Identify the learning type: supervised, unsupervised, or reinforcement. Justify your choice.
- Step 3: Define the inputs. List at least five features.
- Step 4: Define the output. Is it classification or regression? What does it return?
- Step 5: Describe the training data you would need. Where would it come from? How many examples? Who would label it?
- Step 6: Identify one significant limit your system will face — data dependency, potential for confident errors, or lack of understanding — and propose one safeguard.
- Step 7: Write a two-sentence summary of your system that you could explain to a friend who has never studied ML.