Click image to open full size
AI & Machine Learning Basics Cheat Sheet
A printable reference covering AI, machine learning, training data, models, accuracy, loss, classification, regression, and ethics for grades 9-12.
Related Tools
Related Labs
Related Worksheets
Artificial intelligence is the field of making computers perform tasks that usually require human thinking, such as recognizing patterns, making predictions, or understanding language. Machine learning is a major part of AI where a program improves by learning from data instead of following only hand-written rules. Students need this cheat sheet to connect common AI terms with the basic math, data steps, and model ideas used in real systems. It also helps separate useful AI tools from common misconceptions about how they work. The core idea of machine learning is to train a model on examples, test it on new data, and measure how well it performs. Important formulas include accuracy = correct predictions / total predictions, mean squared error = sum of squared errors / n, and precision = TP / (TP + FP). Classification predicts categories, regression predicts numbers, and clustering finds groups without given labels. Good AI work also requires clean data, fair evaluation, privacy awareness, and attention to bias.
Key Facts
- Artificial intelligence is the broader field, and machine learning is one way to build AI systems that learn patterns from data.
- Supervised learning uses labeled examples, where each input has a known correct output, such as image = cat or price = 250000.
- Unsupervised learning uses unlabeled data to find patterns, such as grouping customers with similar behavior.
- Classification predicts a category, while regression predicts a numerical value.
- Accuracy = correct predictions / total predictions, and it is most useful when classes are balanced.
- Mean squared error = sum of (actual value - predicted value)^2 / n, and lower values mean better regression predictions.
- Precision = TP / (TP + FP), and recall = TP / (TP + FN), where TP is true positives, FP is false positives, and FN is false negatives.
- A basic gradient descent update is new weight = old weight - learning rate × slope of loss, which moves the model toward lower error.
Vocabulary
- Artificial Intelligence
- Artificial intelligence is the field of computer science focused on systems that can perform tasks associated with human reasoning, learning, or decision-making.
- Machine Learning
- Machine learning is a method where a computer program learns patterns from data to make predictions or decisions.
- Training Data
- Training data is the set of examples used to teach a machine learning model how inputs relate to outputs.
- Model
- A model is the learned rule, equation, or algorithm that maps inputs to predicted outputs.
- Feature
- A feature is an input variable used by a model, such as age, height, color, word count, or pixel brightness.
- Bias
- Bias is a systematic unfairness or error in data, design, or predictions that can cause a model to treat groups differently.
Common Mistakes to Avoid
- Confusing AI with machine learning is wrong because AI is the broad field, while machine learning is only one approach within it.
- Testing a model on the same data used for training is wrong because it can make performance look better than it really is on new examples.
- Using accuracy alone on an imbalanced dataset is wrong because a model can score high by mostly predicting the majority class.
- Assuming correlation proves causation is wrong because two variables can move together without one directly causing the other.
- Ignoring biased or incomplete data is wrong because a model can learn unfair patterns and repeat them in future predictions.
Practice Questions
- 1 A classifier makes 80 correct predictions out of 100 total predictions. What is its accuracy?
- 2 A regression model has errors of 2, -1, and 3. Compute the mean squared error using mean squared error = sum of errors^2 / n.
- 3 A spam filter has TP = 30, FP = 10, and FN = 5. Compute precision and recall.
- 4 A school wants to use an AI model to predict which students need extra support. Explain two reasons why the training data and evaluation method must be checked carefully before using the model.