Here is the continuation of the comprehensive map of algorithms for extracting patterns from data: - Anomaly Detection - Statistical Methods - Z-Score - Interquartile Range (IQR) - Mahalanobis Distance - Chi-Square Statistic - Grubbs' Test - Kernel Density Estimation (KDE) - Gaussian Mixture Models (GMM) - Hidden Markov Models (HMM) - Machine Learning Methods - Support Vector Machines (One-Class SVM) - Isolation Forest - Local Outlier Factor (LOF) - Connectivity-Based Outlier Factor (COF) - Subspace Outlier Detection (SOD) - Cluster-Based Outlier Detection - DBSCAN - OPTICS - K-Means - Angle-Based Outlier Detection (ABOD) - Feature Bagging - Autoencoder-Based Methods - Generative Adversarial Networks (GANs) - Time Series Anomaly Detection - STL Decomposition - ARIMA Modeling - Holt-Winters Exponential Smoothing - Seasonal Hybrid ESD (S-H-ESD) - Spectral Residual Analysis - Seasonal Trend Decomposition using Loess (STL) - Long Short-Term Memory (LSTM) Networks - Hierarchical Temporal Memory (HTM) - Graph-Based Anomaly Detection - Community-Based Anomaly Detection - Scan Statistics - Frequent Subgraph Mining - Graph Embedding Methods - Spectral Methods - Random Walk-Based Methods 3. Deep Learning - Neural Network Architectures - Multilayer Perceptron (MLP) - Convolutional Neural Networks (CNNs) - AlexNet - VGGNet - GoogLeNet (Inception) - ResNet - DenseNet - Xception - MobileNet - EfficientNet - Capsule Networks - Recurrent Neural Networks (RNNs) - Simple RNN - Long Short-Term Memory (LSTM) - Gated Recurrent Unit (GRU) - Bidirectional RNN - Attention Mechanism - Seq2Seq Models - Neural Turing Machines - Differentiable Neural Computers - Autoencoders - Undercomplete Autoencoders - Sparse Autoencoders - Denoising Autoencoders - Contractive Autoencoders - Variational Autoencoders (VAEs) - Adversarial Autoencoders - Generative Adversarial Networks (GANs) - Vanilla GAN - Conditional GAN (cGAN) - Deep Convolutional GAN (DCGAN) - Wasserstein GAN (WGAN) - Least Squares GAN (LSGAN) - Progressive Growing of GANs (PGGAN) - StyleGAN - CycleGAN - Pix2Pix - StarGAN - Self-Organizing Maps (SOMs) - Hopfield Networks - Boltzmann Machines - Restricted Boltzmann Machines (RBMs) - Deep Belief Networks (DBNs) - Deep Boltzmann Machines (DBMs) - Transformer Models - Attention Is All You Need - BERT (Bidirectional Encoder Representations from Transformers) - GPT (Generative Pre-trained Transformer) - XLNet - RoBERTa - ALBERT - T5 (Text-to-Text Transfer Transformer) - Vision Transformer (ViT) - Neural Network Training Techniques - Gradient Descent - Batch Gradient Descent - Stochastic Gradient Descent (SGD) - Mini-Batch Gradient Descent - Backpropagation - Optimization Algorithms - Momentum - Nesterov Accelerated Gradient (NAG) - Adagrad - Adadelta - RMSprop - Adam - AdaMax - Nadam - AMSGrad - Regularization Techniques - L1 Regularization (Lasso) - L2 Regularization (Ridge) - Elastic Net Regularization - Dropout - Early Stopping - Weight Decay - Batch Normalization - Layer Normalization - Instance Normalization - Group Normalization - Activation Functions - Sigmoid - Hyperbolic Tangent (Tanh) - Rectified Linear Unit (ReLU) - Leaky ReLU - Parametric ReLU (PReLU) - Exponential Linear Unit (ELU) - Scaled Exponential Linear Unit (SELU) - Softmax - Swish - Mish - Loss Functions - Mean Squared Error (MSE) - Mean Absolute Error (MAE) - Huber Loss - Log-Cosh Loss - Binary Cross-Entropy - Categorical Cross-Entropy - Sparse Categorical Cross-Entropy - Kullback-Leibler Divergence - Hinge Loss - Squared Hinge Loss - Cosine Proximity - Connectionist Temporal Classification (CTC) Loss - Hyperparameter Optimization - Grid Search - Random Search - Bayesian Optimization - Hyperband - Evolutionary Algorithms - Gradient-Based Optimization - Neural Architecture Search (NAS) - Reinforcement Learning-Based Methods - Evolutionary Algorithm-Based Methods - Gradient-Based Methods - Bayesian Optimization-Based Methods - One-Shot Models - Differentiable Architecture Search - Transfer Learning - Fine-Tuning - Feature Extraction - Domain Adaptation - Zero-Shot Learning - One-Shot Learning - Few-Shot Learning - Self-Supervised Learning - Distributed and Parallel Training - Data Parallelism - Model Parallelism - Asynchronous SGD - Synchronous SGD - Parameter Server - Ring All-Reduce - Horovod - Distributed TensorFlow - Distributed PyTorch 4. Time Series Analysis - Decomposition Methods - Additive Decomposition - Multiplicative Decomposition - Classical Decomposition - X11 Decomposition - STL Decomposition - Hodrick-Prescott Filter - Beveridge-Nelson Decomposition - Unobserved Components Models - Exponential Smoothing - Simple Exponential Smoothing - Double Exponential Smoothing (Holt's Method) - Triple Exponential Smoothing (Holt-Winters' Method) - Additive Seasonality - Multiplicative Seasonality - Damped Trend Methods - Autoregressive Models - Autoregressive (AR) Model - Moving Average (MA) Model - Autoregressive Moving Average (ARMA) Model - Autoregressive Integrated Moving Average (ARIMA) Model - Seasonal ARIMA (SARIMA) Model - Vector Autoregression (VAR) - Vector Error Correction Model (VECM) - Fractional ARIMA (FARIMA) - Threshold Autoregressive (TAR) Models - Smooth Transition Autoregressive (STAR) Models - State Space Models - Kalman Filter - Extended Kalman Filter - Unscented Kalman Filter - Particle Filter - Hidden Markov Models (HMM) - Dynamic Linear Models (DLM) - Structural Time Series Models - Dynamic Factor Models - Volatility Models - Autoregressive Conditional Heteroskedasticity (ARCH) Model - Generalized ARCH (GARCH) Model - Exponential GARCH (EGARCH) Model - GJR-GARCH Model - Threshold GARCH (TGARCH) Model - Integrated GARCH (IGARCH) Model - Fractionally Integrated GARCH (FIGARCH) Model - Stochastic Volatility (SV) Models - Realized Volatility Models - Machine Learning Models - Random Forest - Gradient Boosting Machines (GBM) - Support Vector Regression (SVR) - Gaussian Process Regression - Recurrent Neural Networks (RNNs) - Long Short-Term Memory (LSTM) - Gated Recurrent Units (GRUs) - Convolutional Neural Networks (CNNs) for Time Series - Temporal Convolutional Networks (TCNs) - Encoder-Decoder Models - Attention Mechanisms - Transformer Models - Ensemble Methods - Bagging - Boosting - Stacking - Hybrid Models - Anomaly Detection in Time Series - Statistical Process Control (SPC) - ARIMA-based Methods - Hierarchical Temporal Memory (HTM) - One-Class SVM - Isolation Forest - Local Outlier Factor (LOF) - Autoencoders - Long Short-Term Memory (LSTM) Networks - Causality Analysis in Time Series - Granger Causality - Transfer Entropy - Convergent Cross Mapping (CCM) - Dynamic Causal Modeling (DCM) - Structural Equation Modeling (SEM) - Forecasting and Prediction - Rolling Window Methods - Recursive Methods - Direct Multi-Step Forecasting - Iterative Multi-Step Forecasting - Forecast Combinations - Forecast Reconciliation - Probabilistic Forecasting - Interval Forecasting - Density Forecasting 5. Anomaly Detection - Statistical Methods - Parametric Methods - Gaussian Distribution - Student's t-Distribution - Gamma Distribution - Poisson Distribution - Non-Parametric Methods - Kernel Density Estimation (KDE) - Histogram-based Methods - k-Nearest Neighbors (k-NN) - Machine Learning Methods - Support Vector Machines (One-Class SVM) - Isolation Forest - Local Outlier Factor (LOF) - Connectivity-Based Outlier Factor (COF) - Angle-Based Outlier Detection (ABOD) - Cluster-Based Methods - DBSCAN - OPTICS - k-Means - Autoencoder-Based Methods - Vanilla Autoencoders - Variational Autoencoders (VAEs) - Denoising Autoencoders - Robust Autoencoders - Ensemble Methods - Feature Bagging - Subsampling - Boosting - Stacking - Subspace-Based Methods - Principal Component Analysis (PCA) - Robust PCA - Kernel PCA - Independent Component Analysis (ICA) - Probabilistic Methods - Gaussian Mixture Models (GMMs) - Hidden Markov Models (HMMs) - Bayesian Networks - Neural Network-Based Methods - Multilayer Perceptron (MLP) - Autoencoders - Generative Adversarial Networks (GANs) - Long Short-Term Memory (LSTM) Networks - Time Series Anomaly Detection - Statistical Process Control (SPC) - ARIMA-based Methods - Hierarchical Temporal Memory (HTM) - One-Class SVM - Isolation Forest - Local Outlier Factor (LOF) - Autoencoders - Long Short-Term Memory (LSTM) Networks - Spatial and Spatiotemporal Anomaly Detection - Spatial Scan Statistics - Spatial Autoregressive Models - Spatial Outlier Detection - Spatiotemporal Scan Statistics - Spatiotemporal Autoregressive Models - Spatiotemporal Outlier Detection - Graph-Based Anomaly Detection - Community-Based Anomaly Detection - Scan Statistics on Graphs - Frequent Subgraph Mining - Graph Embedding Methods - Spectral Methods - Random Walk-Based Methods - Anomaly Explanation and Attribution - Feature Importance Ranking - Shapley Additive Explanations (SHAP) - Local Interpretable Model-Agnostic Explanations (LIME) - Counterfactual Explanations - Influence Functions - Integrated Gradients 6. Natural Language Processing (NLP) - Text Preprocessing - Tokenization - White Space Tokenization - Regular Expression-Based Tokenization - Rule-Based Tokenization - Subword Tokenization (Byte Pair Encoding, WordPiece, SentencePiece) - Normalization - Case Folding - Punctuation Removal - Diacritics Removal - Unicode Normalization - Stop Word Removal - Stemming - Porter Stemmer - Snowball Stemmer - Lancaster Stemmer - Lemmatization - Dictionary-Based Lemmatization - Rule-Based Lemmatization - Probabilistic Lemmatization - Part-of-Speech (POS) Tagging - Rule-Based POS Tagging - Probabilistic POS Tagging (Hidden Markov Models, Maximum Entropy Markov Models, Conditional Random Fields) - Neural Network-Based POS Tagging (Recurrent Neural Networks, Transformers) - Named Entity Recognition (NER) - Rule-Based NER - Machine Learning-Based NER (Conditional Random Fields, Support Vector Machines) - Deep Learning-Based NER (Recurrent Neural Networks, Transformers) - Parsing - Constituency Parsing - Context-Free Grammar (CFG) Parsing - Probabilistic Context-Free Grammar (PCFG) Parsing - Chart Parsing (CYK Algorithm, Earley Algorithm) - Shift-Reduce Parsing - Dependency Parsing - Transition-Based Dependency Parsing - Graph-Based Dependency Parsing - Neural Network-Based Dependency Parsing - Coreference Resolution - Rule-Based Coreference Resolution - Machine Learning-Based Coreference Resolution (Mention-Pair Models, Entity-Mention Models) - Deep Learning-Based Coreference Resolution (Mention Ranking, End-to-End Models) - Text Representation - Bag-of-Words (BoW) - TF-IDF - N-grams - Word Embeddings - Word2Vec (Skip-gram, Continuous Bag-of-Words) - GloVe (Global Vectors for Word Representation) - FastText - Subword Embeddings (Byte Pair Encoding, WordPiece, SentencePiece) - Contextual Word Embeddings - ELMo (Embeddings from Language Models) - BERT (Bidirectional Encoder Representations from Transformers) - GPT (Generative Pre-trained Transformer) - XLNet - RoBERTa - Sentence Embeddings - Paragraph Vectors (Doc2Vec) - Sent2Vec - Universal Sentence Encoder - InferSent - Sentence-BERT - Document Embeddings -