Supervised Learning:

• Regression
• Classification

Unsupervised Learning:

• Clustering
• Dimensionality Reduction
• Association Rule Learning

Semi-supervised Learning

Reinforcement Learning

Deep Learning:

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)
• Transformer Networks

Transfer Learning

Ensemble Learning:

• Bagging (Bootstrap Aggregating)
• Boosting

Self-supervised Learning

Active Learning

Instance-based Learning:

• k-Nearest Neighbors (k-NN)

Decision Tree Learning

Bayesian Methods

Evolutionary Algorithms:

• Genetic Algorithms
• Genetic Programming
• Evolutionary Strategies

Fuzzy Logic

Neuroevolution

Feedforward Neural Networks (FNN):

• The basic form of neural networks where information flows in one direction, from input to output layer, without cycles.

Convolutional Neural Networks (CNN):

• Specialized for processing structured grid data such as images. They consist of convolutional layers that automatically learn hierarchical patterns.

Recurrent Neural Networks (RNN):

• Designed to work with sequence data, such as time series or natural language. They have connections that form loops, allowing information to persist.

Long Short-Term Memory Networks (LSTM):

• A type of RNN designed to overcome the vanishing gradient problem. They are capable of learning long-term dependencies in data.

Gated Recurrent Unit (GRU):

• Another variant of RNNs designed to address the vanishing gradient problem and perform better on some tasks compared to traditional RNNs.

Autoencoder:

• Neural networks designed for unsupervised learning by attempting to learn compressed representations of input data. They consist of an encoder and a decoder.

Generative Adversarial Networks (GAN):

• Comprising two neural networks, a generator and a discriminator, GANs are used for generating new data samples that resemble a given dataset.

Variational Autoencoder (VAE):

• An extension of autoencoders with probabilistic interpretations. VAEs are used for generating new data samples while allowing control over the generation process.

Self-Organizing Maps (SOM):

• Neural networks used for clustering and visualization of high-dimensional data.

Radial Basis Function Networks (RBFN):

• A type of neural network with radial basis functions as activation functions, often used for function approximation and classification tasks.

Echo State Networks (ESN):

• A type of recurrent neural network with a fixed, sparsely connected hidden layer, often used for time-series prediction tasks.

Deep Belief Networks (DBN):

• A type of generative neural network composed of multiple layers of stochastic, latent variables.