1. Artificial Neural Networks (ANNs)

Artificial Neural Networks (ANNs) are computational models inspired by the structure of biological neural networks. They consist of layers of interconnected nodes ("neurons"), where each connection has an associated weight. ANNs are particularly suited for approximating complex nonlinear functions and are foundational to many modern machine learning techniques, especially in deep learning.

The architecture typically includes an input layer, one or more hidden layers, and an output layer. Training is often performed using the backpropagation algorithm and variants of stochastic gradient descent.

2. Perceptron

The perceptron is one of the earliest types of artificial neural networks, developed by Frank Rosenblatt in the 1950s. It is a linear binary classifier that maps input features to a single output using a weighted sum and an activation function, usually a step function.

While the single-layer perceptron can only model linearly separable data, it laid the groundwork for multilayer networks and the broader development of neural learning systems.

3. Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a specialized class of neural networks designed for processing data with grid-like topology, such as images. They use convolutional layers to extract local features, pooling layers for dimensionality reduction, and fully connected layers for classification.

CNNs are widely used in computer vision tasks such as image recognition, object detection, and segmentation. Architectures such as LeNet, AlexNet, VGG, ResNet, and EfficientNet have significantly advanced the field.

4. Reinforcement Learning (RL)

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties and seeks to learn a policy that maximizes cumulative reward over time.

RL is formally modeled using Markov Decision Processes (MDPs). Prominent algorithms include Q-learning, SARSA, and policy gradient methods. Applications range from robotics and game playing (e.g., AlphaGo) to autonomous systems and resource optimization.

5. Logistic Regression

Logistic regression is a supervised learning algorithm used for binary classification. It models the probability that a given input belongs to a particular category using the logistic (sigmoid) function.

Despite the name, logistic regression is a classification algorithm, not a regression one. It is widely used in medical diagnosis, text classification, and social science applications due to its interpretability and statistical grounding.

6. AdaBoost (Adaptive Boosting)

AdaBoost is an ensemble learning technique that combines multiple weak learners to form a strong classifier. It works by iteratively training classifiers, each focusing more on instances misclassified by previous ones.

Commonly used with decision trees as base learners, AdaBoost assigns weights to training samples and updates them at each iteration. The final model is a weighted sum of the individual learners. It is effective in reducing bias and variance and is often used in classification tasks.