## Tags - Part of: [[Artificial Intelligence]] [[Science]] [[Engineering]] [[Science]] [[Technology]] - Related: - Includes: - Additional: ## Definitions - A branch of [[Artificial Intelligence]] that focuses on [[Statistics|statistical]] [[Algorithm|algorithms]] that can effectively generalize and thus perform tasks without explicit instructions. ## Main resources - [Machine learning - Wikipedia](https://en.wikipedia.org/wiki/Machine_learning) <iframe src="https://en.wikipedia.org/wiki/Machine_learning" allow="fullscreen" allowfullscreen="" style="height:100%;width:100%; aspect-ratio: 16 / 5; "></iframe> - Stanford machine learning [https://www.youtube.com/playlist?list=PLoROMvodv4rNyWOpJg_Yh4NSqI4Z4vOYy](https://www.youtube.com/playlist?list=PLoROMvodv4rNyWOpJg_Yh4NSqI4Z4vOYy) https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU , [Machine Learning Specialization \[3 courses\] (Stanford) | Coursera](https://www.coursera.org/specializations/machine-learning-introduction) ## Landscapes - [Outline of machine learning - Wikipedia](https://en.wikipedia.org/wiki/Outline_of_machine_learning) <iframe src="https://en.wikipedia.org/wiki/Outline_of_machine_learning" allow="fullscreen" allowfullscreen="" style="height:100%;width:100%; aspect-ratio: 16 / 5; "></iframe> - By methods - [[Instance-based algorithm]] - [[Regression analysis]] - [[Dimensionality reduction]] - [[Ensemble learning]] - [[Meta learning]] - [[Reinforcement learning]] - [[Supervised learning]] - [[Bayesian statistics]] - [[Decision tree algorithm]] - [[Classifier]] - [[Support-vector machines]] - [[Unsupervised learning]] - [[Artificial neural network]] - [[Association rule learning]] - [[Hiearchical clustering]] - [[Cluster analysis]] - [[Anomaly detection]] - [[Semi-supervised learning]] - [[Deep learning]] - [[AI engineering]] by application - [[Automating science]] - [[Data mining]] - [[Computer vision]] - [[Classification]] - [[Bioinformatics]] - [[Natural language processing]] - [[Large language model]] - [[Transformer]] - [[Large multimodal model]] - [[Pattern recognition]] - [[Recommendation system]] - [[Search engine]] - [[Social engineering]] - [Machine learning Applications - Wikipedia](https://en.wikipedia.org/wiki/Machine_learning#Applications) - Machine learning algorithms - [[Gradient descent]] - [All Machine Learning algorithms explained in 17 min - YouTube](https://www.youtube.com/watch?v=E0Hmnixke2g) <iframe title="Map of Machine Learning" src="https://www.youtube.com/embed/E0Hmnixke2g?feature=oembed" height="113" width="200" allowfullscreen="" allow="fullscreen" style="aspect-ratio: 1.76991 / 1; width: 100%; height: 100%;"></iframe> - [[Images/98bcc7afe4e66c0f5d1d6b65fcc3e519_MD5.jpeg|Open: Pasted image 20241001055944.png]] ![[Images/98bcc7afe4e66c0f5d1d6b65fcc3e519_MD5.jpeg]] - [[Connectionist artificial intelligence]] - ![[Connectionist artificial intelligence#Definitions]] - [[Hybrid AI]] - [[Generative AI]] - [[Quantum machine learning]] - [[Thermodynamic AI]] - [[Mechanistic interpretability]] - [[Mathematical theory of artificial intelligence]] - [[Meta-learning]] - [[Online machine learning]] - [The landscape of the Machine Learning section of ArXiv.](https://twitter.com/leland_mcinnes/status/1731752287788265726) - [[d53207aee25be09f22c9bebc583ac099_MD5.jpeg|Open: Pasted image 20231204230523.png]] ![[d53207aee25be09f22c9bebc583ac099_MD5.jpeg]] ## Lists of resources [Before you continue to YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rNyWOpJg_Yh4NSqI4Z4vOYy) [Before you continue to YouTube](https://www.youtube.com/@stanfordonline/playlists) ## Crossovers ![[Artificial Intelligence#Crossovers]] ## Deep dives - [[Theory of Everything in Intelligence]] - ![[Theory of Everything in Intelligence#Definitions]] ## Landscapes written by AI (may include factually incorrect information) - Machine Learning Algorithms │ ├─ Supervised Learning │ ├─ Classification │ │ ├─ Generalized Linear Models │ │ │ ├─ [[Logistic Regression]] │ │ │ ├─ Probit Regression │ │ │ └─ Multinomial Logistic Regression │ │ ├─ [[Naive Bayes]] │ │ │ ├─ Gaussian Naive Bayes │ │ │ ├─ Multinomial Naive Bayes │ │ │ ├─ Bernoulli Naive Bayes │ │ │ └─ Complement Naive Bayes │ │ ├─ [[Decision Trees]] │ │ │ ├─ ID3 │ │ │ ├─ C4.5 │ │ │ ├─ CART │ │ │ ├─ CHAID │ │ │ └─ Conditional Inference Trees │ │ ├─ Rule-Based Classifiers │ │ │ ├─ OneR │ │ │ ├─ RIPPER │ │ │ └─ PART │ │ ├─ Ensemble Methods │ │ │ ├─ Bagging │ │ │ │ ├─ [[Random Forest]] │ │ │ │ ├─ Extra Trees │ │ │ │ └─ Bagged Decision Trees │ │ │ ├─ [[Boosting]] │ │ │ │ ├─ AdaBoost │ │ │ │ ├─ Gradient Boosting │ │ │ │ │ ├─ XGBoost │ │ │ │ │ ├─ LightGBM │ │ │ │ │ └─ CatBoost │ │ │ │ └─ LogitBoost │ │ │ ├─ Stacking │ │ │ ├─ Voting │ │ │ └─ Cascading │ │ ├─ [[Support Vector Machines]] (SVM) │ │ │ ├─ Linear SVM │ │ │ ├─ Kernel SVM │ │ │ │ ├─ Polynomial Kernel │ │ │ │ ├─ RBF Kernel │ │ │ │ ├─ Sigmoid Kernel │ │ │ │ └─ Custom Kernels │ │ │ ├─ One-Class SVM │ │ │ └─ Multiclass SVM │ │ │ ├─ One-vs-One │ │ │ └─ One-vs-Rest │ │ ├─ K-Nearest Neighbors (KNN) │ │ │ ├─ Brute Force KNN │ │ │ ├─ KD-Trees │ │ │ ├─ Ball Trees │ │ │ └─ Locality Sensitive Hashing (LSH) │ │ ├─ Discriminant Analysis │ │ │ ├─ Linear Discriminant Analysis (LDA) │ │ │ ├─ Quadratic Discriminant Analysis (QDA) │ │ │ └─ Regularized Discriminant Analysis (RDA) │ │ ├─ [[Artificial neural networks]] │ │ │ ├─ Multi-Layer Perceptron (MLP) │ │ │ ├─ [[Convolutional Neural Network]] (CNN) │ │ │ ├─ Capsule Networks │ │ │ └─ [[Spiking Neural Network]] (SNN) │ │ └─ Other Classifiers │ │ ├─ Bayesian Networks │ │ ├─ Gaussian Processes │ │ └─ Relevance Vector Machines (RVM) │ │ │ └─ Regression │ ├─ Linear Models │ │ ├─ [[Linear Regression]] │ │ ├─ [[Polynomial Regression]] │ │ ├─ Stepwise Regression │ │ ├─ LASSO (Least Absolute Shrinkage and Selection Operator) │ │ ├─ Ridge Regression │ │ ├─ Elastic Net │ │ └─ Least-Angle Regression (LARS) │ ├─ Regularization Methods │ │ ├─ L1 Regularization (LASSO) │ │ ├─ L2 Regularization (Ridge) │ │ └─ L1/L2 Regularization (Elastic Net) │ ├─ Decision Trees │ │ ├─ Regression Trees │ │ └─ Model Trees │ ├─ [[Ensemble Methods]] │ │ ├─ Random Forest │ │ ├─ Gradient Boosting (e.g., XGBoost, LightGBM, CatBoost) │ │ ├─ AdaBoost │ │ └─ Stacked Generalization (Stacking) │ ├─ Support Vector Regression (SVR) │ │ ├─ Linear SVR │ │ ├─ Non-Linear SVR │ │ └─ Kernels (e.g., RBF, Polynomial) │ ├─ Gaussian Process Regression (GPR) │ ├─ Isotonic Regression │ ├─ Quantile Regression │ ├─ Kriging (Spatial Interpolation) │ └─ Neural Networks │ ├─ [[Multi-Layer Perceptron]] (MLP) │ ├─ [[Recurrent Neural Networks]] (RNN) │ │ ├─ L[[ong Short-Term Memory]] (LSTM) │ │ └─ [[Gated Recurrent Unit]] (GRU) │ └─ Convolutional Neural Networks (CNN) │ ├─ Unsupervised Learning │ ├─ [[Clustering]] │ │ ├─ [[Partitioning Methods]] │ │ │ ├─ [[K-Means]] │ │ │ ├─ K-Medoids (PAM) │ │ │ ├─ Fuzzy C-Means │ │ │ ├─ Gaussian Mixture Models (GMM) │ │ │ └─ Expectation-Maximization (EM) │ │ ├─ [[Hierarchical Clustering]] │ │ │ ├─ Agglomerative Clustering │ │ │ │ ├─ Single Linkage │ │ │ │ ├─ Complete Linkage │ │ │ │ ├─ Average Linkage │ │ │ │ └─ Ward's Method │ │ │ └─ Divisive Clustering │ │ │ ├─ DIANA │ │ │ └─ DISMEA │ │ ├─ Density-Based Clustering │ │ │ ├─ DBSCAN │ │ │ ├─ OPTICS │ │ │ ├─ HDBSCAN │ │ │ └─ DENCLUE │ │ ├─ Grid-Based Clustering │ │ │ ├─ STING │ │ │ ├─ CLIQUE │ │ │ └─ WaveCluster │ │ ├─ Model-Based Clustering │ │ │ ├─ [[Self-Organizing Maps]] (SOM) │ │ │ ├─ Adaptive Resonance Theory (ART) │ │ │ └─ Deep Embedded Clustering (DEC) │ │ └─ Other Clustering Methods │ │ ├─ Spectral Clustering │ │ ├─ Affinity Propagation │ │ ├─ Mean Shift │ │ └─ BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) │ │ │ ├─ [[Dimensionality Reduction]] │ │ ├─ Linear Methods │ │ │ ├─ Principal Component Analysis (PCA) │ │ │ ├─ Singular Value Decomposition (SVD) │ │ │ ├─ Non-Negative Matrix Factorization (NMF) │ │ │ ├─ Independent Component Analysis (ICA) │ │ │ └─ Factor Analysis │ │ ├─ Non-Linear Methods │ │ │ ├─ [[t-SNE]] (t-Distributed Stochastic Neighbor Embedding) │ │ │ ├─ [[UMAP]] (Uniform Manifold Approximation and Projection) │ │ │ ├─ Locally Linear Embedding (LLE) │ │ │ ├─ Isomap │ │ │ ├─ Laplacian Eigenmaps │ │ │ ├─ Diffusion Maps │ │ │ ├─ Kernel PCA │ │ │ ├─ [[Autoencoder]] │ │ │ │ ├─ Vanilla Autoencoder │ │ │ │ ├─ Denoising Autoencoder │ │ │ │ ├─ [[Sparse Autoencoder]] │ │ │ │ └─ [[Variational Autoencoder]] (VAE) │ │ │ └─ Self-Supervised Learning │ │ │ ├─ Contrastive Learning │ │ │ └─ Clustering-Based Methods │ │ └─ [[Manifold Learning]] │ │ ├─ Multidimensional Scaling (MDS) │ │ ├─ Isomap │ │ ├─ Locally Linear Embedding (LLE) │ │ ├─ Laplacian Eigenmaps │ │ ├─ Hessian Eigenmaps │ │ ├─ Local Tangent Space Alignment (LTSA) │ │ └─ Diffusion Maps │ │ │ └─ Association Rule Learning │ ├─ Apriori │ ├─ FP-Growth │ ├─ Eclat │ └─ GUHA (General Unary Hypotheses Automaton) │ ├─ Semi-Supervised Learning │ ├─ Self-Training │ ├─ Co-Training │ ├─ Tri-Training │ ├─ Transductive SVM │ ├─ Graph-Based Methods │ │ ├─ Label Propagation │ │ └─ Label Spreading │ ├─ Generative Models │ │ ├─ Gaussian Mixture Models (GMM) │ │ └─ Variational Autoencoders (VAE) │ └─ Low-Density Separation │ ├─ Transductive SVM │ └─ S3VM (Semi-Supervised SVM) │ ├─ [[Reinforcement Learning]] │ ├─ [[Model-Free Methods]] │ │ ├─ Value-Based Methods │ │ │ ├─ [[Q-Learning]] │ │ │ ├─ SARSA (State-Action-Reward-State-Action) │ │ │ ├─ Double Q-Learning │ │ │ ├─ Expected SARSA │ │ │ └─ Deep Q-Networks (DQN) │ │ │ ├─ Double DQN │ │ │ ├─ Dueling DQN │ │ │ ├─ Prioritized Experience Replay (PER) │ │ │ └─ Rainbow │ │ └─ Policy-Based Methods │ │ ├─ Policy Gradients │ │ │ ├─ REINFORCE │ │ │ ├─ Advantage Actor-Critic (A2C) │ │ │ ├─ Asynchronous Advantage Actor-Critic (A3C) │ │ │ ├─ Proximal Policy Optimization (PPO) │ │ │ └─ Trust Region Policy Optimization (TRPO) │ │ ├─ Actor-Critic Methods │ │ │ ├─ Deterministic Policy Gradient (DPG) │ │ │ ├─ Deep Deterministic Policy Gradient (DDPG) │ │ │ ├─ Twin Delayed DDPG (TD3) │ │ │ └─ Soft Actor-Critic (SAC) │ │ └─ Entropy-Based Methods │ │ ├─ Soft Q-Learning │ │ └─ Soft Actor-Critic (SAC) │ │ │ └─ Model-Based Methods │ ├─[[ Dynamic Programming]] │ │ ├─ Value Iteration │ │ └─ Policy Iteration │ ├─ [[Monte Carlo Tree Search]] (MCTS) │ ├─ [[AlphaZero]] │ ├─ World Models │ └─ Model-Based RL with Uncertainty │ └─ [[Deep Learning]] ([[Artificial neural networks]]) ├─ [[Feedforward Neural Network]] │ ├─ [[Multi-Layer Perceptron]] (MLP) │ ├─ Extreme Learning Machines (ELM) │ ├─ [[Echo State Network]] (ESN) │ ├─ [[Liquid State Machine]](LSM) │ ├─ [[Spiking Neural Network]] (SNN) │ ├─ [[Autoencoder]] │ │ ├─ Vanilla Autoencoder │ │ ├─ Denoising Autoencoder │ │ ├─ Sparse Autoencoder │ │ ├─ Contractive Autoencoder │ │ ├─ [[Variational Autoencoder]] (VAE) │ │ └─ Adversarial Autoencoder (AAE) │ └─ Deep Belief Networks (DBN) │ ├─ Convolutional Neural Networks (CNN) │ ├─ LeNet │ ├─ AlexNet │ ├─ VGGNet │ ├─ GoogLeNet (Inception) │ ├─ ResNet │ ├─ DenseNet │ ├─ MobileNet │ ├─ EfficientNet │ ├─ Vision Transformers (ViT) │ ├─ Spatial Transformer Networks (STN) │ ├─ Deformable Convolutional Networks (DCN) │ ├─ Capsule Networks │ └─ Attention-Based CNNs │ ├─ Recurrent Neural Networks (RNN) │ ├─ Simple RNN │ ├─ Long Short-Term Memory (LSTM) │ ├─ Gated Recurrent Unit (GRU) │ ├─ Bidirectional RNN │ ├─ Attention Mechanisms │ │ ├─ Seq2Seq with Attention │ │ ├─ [[Transformer]] │ │ │ ├─ BERT (Bidirectional Encoder Representations from Transformers) │ │ │ ├─ GPT (Generative Pre-trained Transformer) │ │ │ ├─ T5 (Text-to-Text Transfer Transformer) │ │ │ ├─ XLNet │ │ │ ├─ RoBERTa │ │ │ ├─ ALBERT │ │ │ ├─ ELECTRA │ │ │ └─ Reformer │ │ └─ Pointer Networks │ ├─ Memory Networks │ ├─ [[Neural Turing Machine]](NTM) │ └─ [[Differentiable Neural Computer]] (DNC) │ ├─ Generative Models │ ├─ [[Generative Adversarial Network]] (GAN) │ │ ├─ DCGAN (Deep Convolutional GAN) │ │ ├─ WGAN (Wasserstein GAN) │ │ ├─ CGAN (Conditional GAN) │ │ ├─ InfoGAN │ │ ├─ Pix2Pix │ │ ├─ CycleGAN │ │ ├─ StarGAN │ │ ├─ Progressive Growing of GANs (PGGAN) │ │ ├─ BigGAN │ │ ├─ StyleGAN │ │ └─ Self-Attention GAN (SAGAN) │ ├─ Variational Autoencoders (VAE) │ │ ├─ Conditional VAE (CVAE) │ │ ├─ Ladder VAE │ │ ├─ VQ-VAE (Vector Quantized VAE) │ │ └─ Disentangled VAE (β-VAE, FactorVAE) │ ├─ Flow-Based Models │ │ ├─ Normalizing Flows │ │ ├─ RealNVP │ │ ├─ Glow │ │ └─ Masked Autoregressive Flow (MAF) │ ├─ Energy-Based Models (EBM) │ └─ Autoregressive Models │ ├─ PixelRNN │ ├─ PixelCNN │ ├─ WaveNet │ └─ Transformer-Based Models (e.g., GPT, CTRL) │ ├─ [[Graph Neural Network]] (GNN) │ ├─ [[Graph Convolutional Network]] (GCN) │ ├─ GraphSAGE │ ├─ [[Graph Attention Network]] (GAT) │ ├─ Graph Isomorphism Network (GIN) │ ├─ Gated Graph Neural Networks (GGNN) │ ├─ Graph Recurrent Networks (GRN) │ ├─ Graph Autoencoders (GAE) │ └─ Graph Generative Models │ └─ [[Deep Reinforcement Learning]] ├─ [[Deep Q-Networks]] (DQN) ├─ [[Policy Gradient Methods]] │ ├─ TRPO (Trust Region Policy Optimization) │ ├─ PPO (Proximal Policy Optimization) │ └─ DDPG (Deep Deterministic Policy Gradient) ├─ Actor-Critic Methods │ ├─ A2C (Advantage Actor-Critic) │ ├─ A3C (Asynchronous Advantage Actor-Critic) │ └─ ACER (Actor-Critic with Experience Replay) ├─ [[Distributional reinforcement learning]] │ ├─ C51 │ └─ QR-DQN (Quantile Regression DQN) ├─ [[Hierarchical reinforcement learning]] │ ├─ Feudal Networks │ ├─ Option-Critic │ └─ MAXQ └─ Inverse Reinforcement Learning (IRL) ├─ Maximum Entropy IRL ├─ Generative Adversarial Imitation Learning (GAIL) └─ Adversarial Inverse Reinforcement Learning (AIRL) - Working with machine learning algorithms 1. Data Preprocessing: - Use NumPy and Pandas for data manipulation and preprocessing. - Scikit-learn provides various tools for data preprocessing, such as scaling, normalization, and encoding categorical variables. 2. Supervised Learning: - Scikit-learn offers implementations of many classic algorithms like linear regression, logistic regression, decision trees, SVMs, and naive Bayes. - For neural networks, you can use libraries like TensorFlow or PyTorch. - XGBoost, LightGBM, and CatBoost are popular libraries for gradient boosting. 3. Unsupervised Learning: - Scikit-learn provides implementations of clustering algorithms like K-means, DBSCAN, and hierarchical clustering. - For dimensionality reduction, you can use PCA, t-SNE, and UMAP from Scikit-learn. - Neural network-based techniques like autoencoders and GANs can be implemented using TensorFlow or PyTorch. 4. Semi-Supervised Learning: - Scikit-learn offers a few semi-supervised learning algorithms, such as label propagation and label spreading. - For more advanced techniques, you may need to implement them from scratch or look for specialized libraries. 5. Reinforcement Learning: - OpenAI Gym is a popular toolkit for developing and comparing reinforcement learning algorithms. - Stable Baselines and RLlib are libraries that provide implementations of various RL algorithms. - For deep reinforcement learning, you can use libraries like TensorFlow or PyTorch in combination with OpenAI Gym. 6. Deep Learning: - TensorFlow and PyTorch are the most widely used libraries for building and training deep neural networks. - Keras is a high-level neural networks API that can run on top of TensorFlow, CNTK, or Theano. - For specific architectures like CNNs, RNNs, and Transformers, these libraries offer pre-built layers and modules. ## Written by AI (may include factually incorrect information) - Machine learning, a branch of [[Artificial Intelligence]], focuses on the development of algorithms and [[Statistics|statistical]] models that enable computers to perform tasks without explicit instructions, relying on patterns and inference instead. It encompasses a wide range of approaches and methodologies. Here's a comprehensive list of various branches and subfields within machine learning: Here is a one-sentence explanation for each entry in your comprehensive list of machine learning branches and subfields: ### 1. Supervised Learning - **Regression (Linear, Polynomial, Logistic):** Involves predicting a continuous output variable based on one or more input features. - **Classification (Decision Trees, Support Vector Machines, k-Nearest Neighbors):** Focuses on categorizing data into predefined classes or groups. - **Ensemble Methods (Random Forests, Boosting, Bagging):** Combines multiple models to improve prediction accuracy or classification performance. - **Neural Networks and Deep Learning:** Complex structures modeled after the human brain that can learn from large amounts of data. - **Bayesian Networks:** Uses probabilistic models for a set of variables and their conditional dependencies. ### 2. Unsupervised Learning - **Clustering (k-Means, Hierarchical, DBSCAN):** Groups similar data points together without predefined labels. - **Dimensionality Reduction (PCA, t-SNE, LDA):** Reduces the number of random variables to consider, simplifying the dataset while retaining important information. - **Association Rule Learning (Apriori, Eclat):** Discovers interesting relations between variables in large databases. - **Anomaly Detection:** Identifies unusual patterns that do not conform to expected behavior. - **Autoencoders:** Neural networks used for unsupervised learning of efficient codings. ### 3. Semi-Supervised Learning - **Self-Training Models:** Use their own predictions to incrementally train on unlabeled data. - **Co-Training Approaches:** Train multiple learners on different views of the data and combine their predictions. - **Label Propagation:** Spreads labels through the dataset based on similarity and distance metrics. - **Generative Models:** Learns to generate new data samples that resemble the given training data. ### 4. Reinforcement Learning - **Q-Learning:** A value-based method for finding the optimal action-selection policy. - **Temporal Difference Methods:** Learn directly from raw experience without a model of the environment’s dynamics. - **Deep Reinforcement Learning:** Combines deep neural networks with reinforcement learning. - **Policy Optimization:** Focuses on finding the best policy directly, rather than evaluating a given policy. - **Multi-Armed Bandit Algorithms:** Solves problems where you have to choose between multiple options with uncertain outcomes. - **Monte Carlo Tree Search:** A heuristic search algorithm for decision-making processes, particularly in game playing. ### 5. Deep Learning - **Convolutional Neural Networks (CNNs):** Specialized for processing data with a grid-like topology, such as images. - **Recurrent Neural Networks (RNNs):** Designed for processing sequential data, such as time series or natural language. - **Long Short-Term Memory Networks (LSTMs):** An advanced type of RNN capable of learning long-term dependencies. - **Generative Adversarial Networks (GANs):** Consists of two neural networks contesting with each other to generate new, synthetic instances of data. - **Transformer Models:** Utilizes attention mechanisms to significantly improve the quality of results in NLP tasks. - **Deep Reinforcement Learning:** Integrates deep learning and reinforcement learning principles for complex problem-solving. - **Autoencoders and Variational Autoencoders:** Used for learning efficient codings of input data. ### 6. [[Natural language processing]] (NLP) - **Text Classification:** Assigns categories or labels to text based on its content. - **Sentiment Analysis:** Identifies and categorizes opinions expressed in text to determine the writer's attitude. - **Machine Translation:** Automatically translates text or speech from one language to another. - **Speech Recognition:** Converts spoken language into text. - **Language Generation:** Creates meaningful phrases, sentences, or entire articles. - **Named Entity Recognition:** Identifies and classifies key information (entities) in text. - **Topic Modeling:** Discovers abstract topics within a collection of documents. ### 7. Computer Vision - **Image Classification:** Assigns a label to an entire image or photograph. - **Object Detection:** Identifies and locates objects within an image. - **Image Segmentation:** Divides a digital image into multiple segments to simplify its representation. - **Face Recognition:** Identifies or verifies a person from a digital image or a video frame. - **Optical Character Recognition:** Converts different types of documents into editable and searchable data. - **Image Generation:** Creates new images, often from a given set of conditions or attributes. ### 8. Predictive Analytics - **Time Series Analysis:** Analyzes time-ordered sequence data to extract meaningful statistics and characteristics. - **Forecasting Models:** Predicts future values based on previously observed values . - **Survival Analysis:** Analyzes and predicts the time until an event of interest occurs. - **Anomaly Detection in Time Series:** Identifies unusual patterns in time-ordered data that do not conform to expected behavior. ### 9. Recommender Systems - **Content-Based Filtering:** Recommends items similar to those a user likes, based on their previous actions or explicit feedback. - **Collaborative Filtering:** Makes automatic predictions about user interests by collecting preferences from many users. - **Hybrid Recommender Systems:** Combines content-based and collaborative filtering methods to improve recommendation accuracy. ### 10. Bayesian Learning - **Bayesian Networks:** Graphical models that represent probabilistic relationships among variables. - **Gaussian Processes:** A flexible approach to regression problems. - **Markov Chain Monte Carlo (MCMC) Methods:** A class of algorithms for sampling from probability distributions. - **Naive Bayes Classifiers:** A simple probabilistic classifier based on Bayes' theorem with strong independence assumptions. ### 11. Evolutionary Algorithms - **Genetic Algorithms:** Mimics the process of natural selection to solve optimization and search problems. - **Evolutionary Strategies:** Uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. - **Genetic Programming:** Evolves computer programs to perform a specific task. ### 12. Feature Engineering and Selection - **Feature Extraction:** Reduces the number of resources required to describe a large set of data accurately. - **Feature Importance:** Identifies which features are most relevant to the outcome of a particular predictive model. - **Regularization Techniques (L1, L2):** Methods used to prevent overfitting by penalizing large coefficients in the model. ### 13. Interpretability and Explainability - **Model Interpretation Methods:** Techniques that make the outputs of machine learning models understandable to humans. - **Explainable AI (XAI) Techniques:** Strives to make the results of AI and machine learning algorithms transparent and understandable. - **Feature Importance Analysis:** Identifies and ranks the importance of different inputs to a model. ### 14. Ensemble Methods - **Boosting (AdaBoost, Gradient Boosting):** Combines weak learners to create a strong learner in a sequential manner. - **Bagging (Random Forest):** Uses bootstrapping to create an ensemble of models and then averages their predictions. - **Stacking:** Combines multiple classification or regression models via a meta-classifier or a meta-regressor. ### 15. Transfer Learning - **Domain Adaptation:** Adapts a model trained in one domain to be effective in a different domain. - **Fine-Tuning Pretrained Models:** Adjusts a pre-existing model to make it perform better in a specific task. - **Multi-Task Learning:** Improves learning efficiency and prediction accuracy for one task by using the knowledge gained while solving related tasks. ### 16. Distributed and Parallel Machine Learning - **Big Data Analytics:** Processes large volumes of data to extract useful information and insights. - **Scalable Machine Learning Algorithms:** Designed to handle increasing amounts of data or computation efficiently. - **Cloud-Based Machine Learning:** Utilizes cloud computing resources to build, train, and deploy machine learning models. ### 17. Optimization Techniques in Machine Learning - **Gradient Descent and Variants:** An iterative optimization algorithm used to minimize a function by moving in the direction of steepest descent. - **Stochastic Optimization:** Optimization methods that use randomness as part of the solution process. - **Convex Optimization:** A subfield of optimization that studies the problem of minimizing convex functions over convex sets. ### 18. Anomaly and Outlier Detection - **Statistical Methods for Anomaly Detection:** Identifies anomalies based on statistical models. - **Isolation Forest:** An algorithm to detect outliers that isolates anomalies instead of profiling normal data points. - **One-Class SVM:** A variant of SVM that is used for anomaly detection in an unsupervised manner. ### 19. Audio and Speech Processing - **Speech Recognition:** Transforms spoken language into text by computers. - **Music Classification:** Categorizes music into genres, moods, or other attributes using machine learning. - **Sound Generation:** Creates synthetic sounds or music. ### 20. Robotics and Control Systems - **Machine Learning in Robotics:** Applies machine learning techniques to enable robots to learn from and adapt to their environment. - **Control Systems Using Reinforcement Learning:** Uses reinforcement learning to optimize the performance of control systems. Machine learning is a rapidly evolving field, continually incorporating new algorithms, techniques, and applications. Its versatility allows it to be applied across various domains, including finance, healthcare, education, transportation, and more, making it a pivotal technology in the modern world.