Fundamentals of Machine Learning

Machine Learning Revision Sheet

1. 📌 Essentials

• Machine learning creates models that predict outcomes based on data.
• Models are functions y = f(x), trained on labeled datasets.
• Features (x) are input variables; labels (y) are targets.
• Types include regression, classification, clustering, and deep learning.
• Regression predicts continuous values; classification predicts categories.
• Deep learning uses neural networks with layered architectures- Model evaluation uses metrics like MAE, MSE, accuracy, F1-score.
• Training involves data to derive the function; inference predicts new data.
• Supervised learning uses labeled data; unsupervised learning finds patterns without labels.
• Common algorithms: Linear regression, logistic regression, K-Means, neural networks.

2. 🧩 Key Structures & Components

• Training Data — past observations with features and labels.
• Features (x) — input attributes, e.g., temperature, measurements.
• Labels (y) — target output, e.g., sales, species, risk.
• Model — the learned function y = f(x).
• Algorithm — method to fit data, e.g., gradient descent.
• Neural Network — layered structure mimicking neurons for deep learning.
• Loss Function — measures prediction error during training.
• Evaluation Metrics — assess model performance (e.g., accuracy, R2).

3. 🔬 Functions, Mechanisms & Relationships