Groups dense regions, marks low-density points as outliers.

Concepts:
- $\epsilon$ (eps): Neighborhood radius.
- MinPts: Min points for dense region.
- Core Point: $\ge$ MinPts within $\epsilon$.
- Border Point: Reachable from core, but not core itself.
- Noise Point: Neither core nor border.
Working: Pick unvisited point $\rightarrow$ Find $\epsilon$-neighborhood $\rightarrow$ If core, start cluster and expand $\rightarrow$ If not core, mark noise.
Advantages: Finds arbitrary shapes, robust to outliers, no pre-specified $k$.
Limitations: Struggles with varying densities, sensitive to $\epsilon$ and MinPts.

Probability of event A given event B occurred: $P(A|B)$.

Formula: $P(A|B) = P(A \text{ and } B) / P(B)$, if $P(B) > 0$.
Example: Drawing a King given it's a Heart from a deck.
- $P(\text{Heart}) = 13/52 = 1/4$.
- $P(\text{King and Heart}) = 1/52$.
- $P(\text{King}|\text{Heart}) = (1/52) / (13/52) = 1/13$.

Probabilistic classifier based on Bayes' Theorem with strong feature independence assumption.

How it works:
1. Bayes' Theorem: $P(\text{Class}|\text{Features}) = [P(\text{Features}|\text{Class}) * P(\text{Class})] / P(\text{Features})$.
2. "Naive" Assumption: $P(\text{Features}|\text{Class}) = \prod P(f_i|\text{Class})$.
3. Train: Calculate priors $P(\text{Class})$ and likelihoods $P(f_i|\text{Class})$.
4. Predict: Choose class with highest posterior probability.
Types: Gaussian, Multinomial, Bernoulli.
Advantages: Fast, simple, scalable, works well with categorical/high-dimensional data.
Disadvantages: Naive independence assumption, zero-frequency problem (solved by Laplace smoothing).
Applications: Spam filtering, sentiment analysis, document categorization.

Computational model inspired by biological neurons, organized in layers.

Architecture: Input Layer $\rightarrow$ Hidden Layers $\rightarrow$ Output Layer.
Neuron: Input $(x)$, weights $(w)$, bias $(b) \rightarrow$ Weighted sum $z = \sum(w_i x_i) + b \rightarrow$ Activation function $f(z)$ (e.g., ReLU, Sigmoid).
Forward Propagation: Data passes through layers to compute output.
Loss Function: Compares output to true value (e.g., MSE, Cross-Entropy).
Backpropagation: Calculates gradient of loss w.r.t. weights by propagating error backward.
Optimization: Updates weights using Gradient Descent: $w_{new} = w_{old} - \eta * (\partial Loss/\partial w)$.
Iteration: Repeat until convergence.