1) Supervised learning

You have inputs X and the correct outputs y.

Goal: learn a function f(X) ≈ yf(X) ≈ y.

Examples:

• Regression: predict a number (house price)
• Classification: predict a category (spam vs not spam)

  flowchart LR
  X[Features (X)] --> M[Model] --> Y[Prediction (y_hat)]
  YT[True Label (y)] -. used for training .-> M

Common algorithms:

• linear/logistic regression
• KNN
• SVM
• decision trees, random forests
• gradient boosting

2) Unsupervised learning

You have inputs X but no labels.

Goal: discover structure.

Examples:

• clustering customers into groups
• anomaly detection
• dimensionality reduction (PCA)

  flowchart LR
  X[Data (X)] --> A[Unsupervised Algorithm] --> S[Structure / Groups]

Common algorithms:

• k-means
• hierarchical clustering
• DBSCAN
• PCA

3) Reinforcement learning

You have an agent acting in an environment.

The agent learns by trial and error to maximize reward.

Examples:

• robotics
• game-playing agents
• dynamic resource allocation

  flowchart LR
  Agent -->|action| Env[Environment]
  Env -->|state, reward| Agent

Key terms:

• state: what the agent observes
• action: what the agent does
• reward: feedback signal
• policy: strategy mapping states → actions

Quick comparison table

Mini-checkpoint

Pick a real problem and decide:

• supervised / unsupervised / RL
• what your XX and yy would be

🧪 Try It Yourself

Exercise 1 – Train-Test Split

Exercise 2 – Fit a Linear Model

Exercise 3 – Evaluate with MSE