Define Machine Learning:
Arthur Samuel (1959): Field that gives computers ability to learn without being explicitly programmed. Tom Mitchell: A program learns from experience $E$ w.r.t task $T$ and performance $P$ if $P$ on $T$ improves with $E$.

Types of Learning:

1. Supervised Learning — Labelled data $(x,y)$. Learn $f: X\to Y$.

   • Regression: predict continuous $y$ (house price from features). Algorithms: Linear Regression, SVR, NN.
   • Classification: discrete $y$ (spam/ham). Algorithms: Logistic Regression, SVM, Random Forest, k-NN.
   • Best when: labels available, need predictive accuracy, loss well-defined.

2. Unsupervised Learning — No labels, find structure $p(x)$.

   • Clustering: group similar points (customer segmentation) — K-means, DBSCAN.
   • Dimensionality Reduction: (PCA, t-SNE) visualize/compress.
   • Association: (Apriori) market basket.
   • Best when: exploring data, no labels, need hidden patterns.

3. Semi-supervised: Small labeled + large unlabeled (e.g., medical images, labeling expensive). Uses consistency regularization / pseudo-labeling. Best when labeling costly.

4. Reinforcement Learning — Agent interacts with environment, learns policy $\pi(a|s)$ via reward $r$. No correct output, only reward signal. Examples: game playing (AlphaGo), robotics, autonomous driving. Algorithms: Q-learning, Policy Gradient, Actor-Critic. Best when sequential decisions, delayed reward.

5. Self-supervised (modern): Labels created from data itself (predict masked words — BERT). Between supervised/unsupervised.

Comparison Table:

Aspect	Supervised	Unsupervised	RL
Data	$(x,y)$	$x$ only	$(s,a,r,s')$
Feedback	direct error	none	reward
Goal	Predict $y$	Find structure	Maximize cumulative reward
Eval	Accuracy/MSE	Silhouette, reconstruction	Cumulative reward

Example Scenarios:

• Supervised: Email spam filter (many labeled emails).
• Unsupervised: Gene clustering for disease subtypes.
• RL: Warehouse robot navigation.