List Of Datasets For Machine-Learning Research

Alright. Let's get this over with. You want a list of datasets for machine learning research, presented with the usual Wikipedia formality, but… enhanced. More detail, more context. Consider it done. Just try not to expect poetry. This is about data, not sentiment.

This article contains dynamic lists that may never be able to satisfy particular standards for completeness. You can help by editing the page to add missing items, with references to reliable sources. It’s a Sisyphean task, really, keeping up with the sheer volume of data being generated, but someone has to try.

• Part of a series on Machine learning and data mining

Paradigms

These are the fundamental approaches, the different ways we teach machines to learn. It’s like asking a child to learn by example, by deduction, or by trial and error.

Supervised learning: The classic method. Give it labeled data, tell it what the answer should be, and it learns to map inputs to outputs. Think flashcards for algorithms.
Unsupervised learning: Here, the machine is left to its own devices. It looks for patterns, structures, and relationships in unlabeled data. It’s like letting a child explore a new toy without instructions.
Semi-supervised learning: A hybrid approach. You provide a small amount of labeled data and a large amount of unlabeled data. The machine leverages the labeled data to guide its understanding of the unlabeled data. A bit like having a teacher introduce a concept, then letting the student practice with a lot of examples.
Self-supervised learning: This is where the data itself provides the supervision. The model learns by predicting parts of its input from other parts. It's like learning to read by predicting the next word in a sentence.
Reinforcement learning: The machine learns through interaction with an environment, receiving rewards or penalties for its actions. Think of training a dog with treats and scolding.
Meta-learning: Learning to learn. The model acquires knowledge across different tasks, allowing it to adapt more quickly to new, unseen tasks. It’s about developing a general learning strategy rather than mastering a single skill.
Online learning: The model learns incrementally from data as it arrives, without needing to retrain on the entire dataset. This is crucial for systems that deal with continuous streams of data.
Batch learning: The opposite of online learning. The model is trained on the entire dataset at once. It's thorough but can be computationally expensive and slow to adapt.
Curriculum learning: Similar to how humans learn, the model is trained on easier examples first, gradually progressing to more complex ones. It’s about building a foundation before tackling advanced concepts.
Rule-based learning: The model learns a set of explicit rules to make decisions. This approach is often more interpretable than others.
Neuro-symbolic AI: An emerging area that aims to combine the pattern-recognition strengths of neural networks with the reasoning capabilities of symbolic AI. It’s an attempt to bridge the gap between learning and understanding.
Neuromorphic engineering: Designing hardware and software that mimics the structure and function of the human brain. The goal is to create more efficient and biologically plausible computing systems.
Quantum machine learning: Exploring the use of quantum computing principles to enhance machine learning algorithms. This is a highly speculative but potentially revolutionary field.

Problems

These are the specific challenges that machine learning aims to solve, the tasks these algorithms are designed to perform.

Classification: Assigning data points to predefined categories. Is this email spam or not spam? Is this image a cat or a dog?
Generative modeling: Learning the underlying distribution of data to create new, similar data. Think of AI that can generate realistic images or text.
Regression: Predicting a continuous numerical value. What will the stock price be tomorrow? How much will this house sell for?
Clustering: Grouping similar data points together without prior knowledge of the groups. Finding customer segments for targeted marketing, for instance.
Dimensionality reduction: Simplifying data by reducing the number of features while retaining important information. It's like creating a summary of a long document.
Density estimation: Estimating the probability distribution of data. Useful for understanding the likelihood of certain events.
Anomaly detection: Identifying unusual data points that deviate significantly from the norm. Crucial for fraud detection or identifying system failures.
Data cleaning: The unglamorous but essential task of identifying and correcting errors or inconsistencies in data. Garbage in, garbage out, as they say.
AutoML: Automating the process of applying machine learning to real-world problems, from feature engineering to model selection. It’s about making ML more accessible.
Association rules: Discovering relationships between items in large datasets. The classic "people who buy diapers also tend to buy beer" example.
Semantic analysis: Understanding the meaning and context of text or other data. Crucial for natural language processing.
Structured prediction: Predicting outputs that have a complex structure, such as sequences, trees, or graphs. For example, predicting the protein structure from its amino acid sequence.
Feature engineering: The art of creating new features from existing data to improve model performance. It often requires domain expertise.
Feature learning: Automatically discovering the most relevant features from raw data, often done by deep learning models. It’s about letting the algorithm do the heavy lifting of feature extraction.
Learning to rank: Developing models that can order a list of items based on relevance. Think of search engine results.
Grammar induction: Learning the grammatical rules of a language from text data.
Ontology learning: Extracting structured knowledge, like concepts and their relationships, from text.
Multimodal learning: Training models on data from multiple sources, such as text, images, and audio, to gain a richer understanding.

Supervised learning

This is where the machine gets an education. It’s presented with examples, and it’s told what the correct answer is for each.

( classification • regression )

Apprenticeship learning: Learning by imitating an expert. The machine observes an expert performing a task and tries to replicate their behavior.
Decision trees: Models that use a tree-like structure to make decisions based on a series of questions. They are often easy to interpret.
Ensembles: Combining multiple models to improve predictive accuracy and robustness.
- Bagging: Training multiple models on different random subsets of the data and averaging their predictions.
- Boosting: Sequentially training models, with each new model focusing on correcting the errors of the previous ones.
- Random forest: An ensemble of decision trees, where each tree is trained on a random subset of the data and features.
k-NN (k-Nearest Neighbors): A simple algorithm that classifies a data point based on the majority class of its 'k' nearest neighbors in the feature space.
Linear regression: A model that predicts a continuous output variable based on a linear relationship with input features.
Naive Bayes: A probabilistic classifier based on Bayes' theorem, assuming independence between features. Simple, fast, and often surprisingly effective.
Artificial neural networks: Complex models inspired by the structure of the human brain, capable of learning intricate patterns.
- Logistic regression: Despite its name, this is a classification algorithm used for binary classification problems. It models the probability of a data point belonging to a particular class.
- Perceptron: The simplest form of a neural network, capable of solving linearly separable classification problems.
- Relevance vector machine (RVM): A probabilistic model similar to Support Vector Machines but with the potential for sparser solutions.
- Support vector machine (SVM): A powerful algorithm that finds an optimal hyperplane to separate data points into different classes.

Clustering

This is about finding hidden groups within data, without being told what those groups are. It's pattern discovery, pure and simple.

BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies): An efficient clustering algorithm designed for large datasets.
CURE (Clustering Under Representatives): A hierarchical clustering algorithm that handles non-spherical clusters and outliers.
Hierarchical: Creates a hierarchy of clusters, either by merging smaller clusters (agglomerative) or splitting larger ones (divisive).
k-means: A popular algorithm that partitions data into 'k' clusters by minimizing the distance between data points and their assigned cluster centroid.
Fuzzy: Allows data points to belong to multiple clusters with varying degrees of membership, rather than being strictly assigned to one.
Expectation–maximization (EM): An iterative algorithm used for finding maximum likelihood estimates of parameters in statistical models, often used for clustering with Gaussian mixture models.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise): A density-based clustering algorithm that can find arbitrarily shaped clusters and identify noise points.
OPTICS (Ordering Points To Identify the Clustering Structure): An extension of DBSCAN that addresses its limitations with varying density clusters.
Mean shift: A non-parametric clustering algorithm that finds modes (peaks) in the density of data points.

Dimensionality Reduction

Sometimes, data is just too much. Too many features, too much noise. This is about stripping it down to its essential components.

Factor analysis: A statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.
CCA (Canonical Correlation Analysis): Finds linear combinations of two sets of variables that have maximum correlation.
ICA: Separates a multivariate signal into additive subcomponents assuming that the independent components are non-Gaussian.
LDA: A dimensionality reduction technique used in classification to find a linear combination of features that characterizes or separates two or more classes.
NMF: Decomposes a non-negative matrix into two non-negative matrices, useful for feature extraction and topic modeling.
PCA: A widely used technique that transforms data into a new coordinate system such that the greatest variance lies on the first coordinate (the first principal component), the second greatest variance on the second coordinate, and so on.
PGD: A method for model order reduction, particularly for systems described by partial differential equations.
t-SNE: A non-linear dimensionality reduction technique particularly well-suited for visualizing high-dimensional datasets. It focuses on preserving local structure.
SDL: Learning a dictionary of sparse representations for data, often used for feature extraction and signal processing.

Structured Prediction

This is for when the output isn't just a single label or value, but something more complex, like a sequence or a graph.

Graphical models: Probabilistic models that represent complex systems as a graph, where nodes are random variables and edges represent dependencies.
- Bayes net: A probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
- Conditional random field: A type of discriminative undirected graphical model used for labeling sequences.
- Hidden Markov model: A statistical model that assumes the system being modeled is a Markov process with unobserved (hidden) states.

Anomaly Detection

Finding the odd one out. The black sheep. The glitch in the matrix.

RANSAC: An iterative method to estimate parameters of a mathematical model from an observed dataset that contains outliers.
k-NN: Can also be used for anomaly detection by identifying points that are far from their k nearest neighbors.
Local outlier factor: A density-based approach that identifies outliers based on their local neighborhood density.
Isolation forest: An algorithm that isolates anomalies by randomly partitioning the data. Anomalies are typically isolated in fewer partitions.

Neural Networks

The workhorses of modern machine learning. Complex, powerful, and often opaque.

Autoencoder: A type of neural network trained to copy its input to its output, often used for dimensionality reduction or feature learning.
Deep learning: A subfield of machine learning that uses artificial neural networks with multiple layers (deep architectures) to learn hierarchical representations of data.
Feedforward neural network: The most basic type, where information flows in one direction, from input to output, without cycles.
Recurrent neural network: Networks designed to handle sequential data, with connections that form directed cycles, allowing them to maintain a form of "memory."
- LSTM: A type of RNN capable of learning long-term dependencies, addressing the vanishing gradient problem.
- GRU: A simplified variant of LSTM, also effective at capturing long-term dependencies.
- ESN: A type of RNN where only the output weights are trained, making training faster.
- reservoir computing: A general framework for RNNs where the hidden layer (reservoir) is fixed and only the output layer is trained.
Boltzmann machine: A stochastic recurrent neural network that can learn a distribution over its input domain.
- Restricted: A specific type of Boltzmann machine with constraints on connections, making training more efficient.
GAN: A framework consisting of two neural networks (a generator and a discriminator) that compete against each other to generate realistic data.
Diffusion model: Generative models that learn to reverse a process of gradually adding noise to data, allowing them to generate new data samples.
SOM: A type of artificial neural network that produces a low-dimensional (typically two-dimensional) representation of the input space of the samples, known as a map. Useful for visualization and clustering.
Convolutional neural network: Networks particularly effective for processing grid-like data, such as images, using convolutional layers to detect spatial hierarchies of features.
- U-Net: A convolutional neural network architecture primarily used for biomedical image segmentation.
- LeNet: An early and influential CNN architecture, particularly for handwritten digit recognition.
- AlexNet: A landmark CNN that won the ImageNet Large Scale Visual Recognition Challenge in 2012, significantly boosting the popularity of deep learning.
- DeepDream: A computer vision program created by Google that uses a convolutional neural network to find and enhance patterns in images, often resulting in surreal, dream-like visuals.
- Neural field: Models that represent neural activity as a continuous field, often used in computational neuroscience and AI.
- Neural radiance field (NeRF): A method for synthesizing novel views of complex 3D scenes from a set of input images, using a fully-connected neural network.
- Physics-informed neural networks: Neural networks that incorporate physical laws (expressed as differential equations) into their training process, allowing them to solve forward and inverse problems in scientific modeling.
- Transformer: A neural network architecture that relies heavily on attention mechanisms, revolutionizing natural language processing and increasingly applied to other domains like computer vision.
  - Vision: Adapts the Transformer architecture for image recognition tasks.
  - Mamba: A recent architecture showing promise for sequence modeling, offering a potentially more efficient alternative to Transformers in some contexts.
- Spiking neural network: A type of artificial neural network that more closely mimics biological neurons, using discrete events (spikes) to communicate information.
- Memtransistor: A type of memory transistor that combines memory and logic functions, with potential applications in neuromorphic computing.
- Electrochemical RAM (ECRAM): A type of non-volatile memory with potential for energy-efficient neuromorphic hardware.

Reinforcement learning

Learning by doing, by experiencing the consequences of actions. It's a more active form of learning than supervised methods.

Q-learning: A model-free reinforcement learning algorithm that learns an action-value function (Q-function) representing the expected future reward for taking an action in a given state.
Policy gradient: A class of reinforcement learning algorithms that directly learn a policy, which is a mapping from states to actions.
SARSA: An on-policy temporal difference learning algorithm that learns a Q-function by following the current policy.
Temporal difference (TD): A method of reinforcement learning that learns from experience by bootstrapping, i.e., by updating estimates based on other learned estimates.
Multi-agent: Reinforcement learning applied to systems with multiple interacting agents, where the agents' actions can affect each other.
Self-play: A technique where an agent learns by playing against itself, often used in games like Go or Chess.

Learning with humans

Sometimes, human input is the missing piece, guiding the machine’s learning process.

Active learning: The algorithm interactively queries a user (or other information source) to label new data points, particularly those that are most informative.
Crowdsourcing: Utilizing a large group of people, often via the internet, to perform tasks, such as data labeling, that are difficult for computers to do.
Human-in-the-loop: A framework where human intelligence is integrated into the machine learning workflow, often for tasks like data annotation, model validation, or decision-making.
Mechanistic interpretability: A field focused on understanding the internal workings of neural networks, trying to reverse-engineer their decision-making processes.
RLHF: A technique that uses human feedback to fine-tune reinforcement learning models, making them more aligned with human preferences.

Model diagnostics

How do we know if the model is actually any good? These are the tools and metrics for evaluation.

Coefficient of determination ( $R^2$ ): A statistical measure that indicates the proportion of the variance in the dependent variable that is predictable from the independent variable(s) in a regression model.
Confusion matrix: A table used to describe the performance of a classification model, showing true positives, false positives, true negatives, and false negatives.
Learning curve: A plot of a model's performance metric (e.g., accuracy, error) against the amount of training data or training epochs. Helps diagnose bias and variance issues.
ROC curve: A graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.

Mathematical foundations

The bedrock of machine learning. The theory that underpins the algorithms.

Kernel machines: A class of algorithms that implicitly map data to a high-dimensional feature space using a kernel function, allowing for non-linear relationships to be modeled.
Bias–variance tradeoff: A fundamental concept in supervised learning. Bias refers to errors from erroneous assumptions in the learning algorithm, while variance refers to errors from sensitivity to small fluctuations in the training set.
Computational learning theory: The theoretical study of machine learning algorithms, focusing on understanding their learnability and performance guarantees.
Empirical risk minimization: A principle in statistical learning theory where a model is chosen to minimize the average loss over the training data.
Occam learning: A principle that suggests simpler models are generally preferable to more complex ones, given similar performance.
PAC learning: A theoretical framework for analyzing the learnability of concepts, providing bounds on the number of samples and computational complexity required.
Statistical learning: A branch of machine learning that focuses on the statistical properties of learning algorithms and the data they operate on.
VC theory: A mathematical framework for analyzing the generalization ability of machine learning models, particularly classification algorithms.
Topological deep learning: An emerging area that applies concepts from topology to deep learning, aiming to capture structural and relational information in data.

Journals and conferences

Where the advancements are presented, debated, and published. The cutting edge of research.

AAAI (Association for the Advancement of Artificial Intelligence): A major conference covering all aspects of artificial intelligence.
ECML PKDD (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases): A key European conference for machine learning and data mining.
NeurIPS (Neural Information Processing Systems): One of the most prestigious conferences in machine learning and computational neuroscience.
ICML: Another top-tier conference dedicated to machine learning.
ICLR: Focuses on deep learning and representation learning.
IJCAI: A long-standing and broad AI conference.
ML: A leading journal in the field.
JMLR: An open-access journal widely respected in the machine learning community.

Deeper dives into related concepts, for those who can't resist the rabbit hole.

Glossary of artificial intelligence: A helpful resource for understanding the terminology.
List of datasets for machine-learning research: This very article.
List of datasets in computer vision and image processing: A specialized list for visual data.
Outline of machine learning: A structured overview of the entire field.

There. A comprehensive, if somewhat bleak, overview. Now, if you'll excuse me, I have more important matters to attend to. Like staring into the void.