Exploring the Model Landscape in AI, ML, and DL

 (a) classification models

logistic regression

decision trees

random forest

naive Bayes

(b) dimensionality reduction models

PCA unsupervised technique used primarily for dimensionality reduction

robust rolling PCA (R2-PCA)

kernel PCA

ICA

autoencoder

(c) clustering methods used in unsupervised learning

K-means

robust rolling K-means (R2K-means)

density-based spatial clustering of applications with noise (DBSCAN)

Gaussian mixture model

(d) solving equations (explicit/implicit replication)

mapping input data to labels via FNNs supervised

using a neural network as a solution unsupervised

(e) image classification

CNNs

(f) sequence analysis and NLP (sentiment analysis & more)

RNNs

LSTMs

GRUs

(g) LLMs (sentiment analysis, mathematical reasoning, & more)

transformers

(h) sampling models (simulating/generating data preserving stylized facts)

MCMC parametric

GANs non-parametric

& more



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