## 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.