Machine learning and data mining
Part of a series on
Paradigms Supervised learning Unsupervised learning Online learning Batch learning Meta-learning Semi-supervised learning Self-supervised learning Reinforcement learning Rule-based learning Quantum machine learning
Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured prediction Feature engineering Feature learning Learning to rank Grammar induction Ontology learning Multimodal learning
Supervised learning (classification • regression) Apprenticeship learning Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Fuzzy Expectation–maximization (EM) DBSCAN OPTICS Mean shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE SDL
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection RANSAC k-NN Local outlier factor Isolation forest
Artificial neural network Autoencoder Cognitive computing Deep learning DeepDream Feedforward neural network Recurrent neural network LSTM GRU ESN reservoir computing Restricted Boltzmann machine GAN Diffusion model SOM Convolutional neural network U-Net Transformer Vision Mamba Spiking neural network Memtransistor Electrochemical RAM (ECRAM)
Reinforcement learning Q-learning SARSA Temporal difference (TD) Multi-agent Self-play
Learning with humans Active learning Crowdsourcing Human-in-the-loop RLHF
Model diagnostics Coefficient of determination Confusion matrix Learning curve ROC curve
Mathematical foundations Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine-learning venues ECML PKDD NeurIPS ICML ICLR IJCAI ML JMLR
Related articles Glossary of artificial intelligence List of datasets for machine-learning research List of datasets in computer vision and image processing Outline of machine learning
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The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research.^[1] It is supported by the International Machine Learning Society (IMLS). Precise dates vary year to year, but paper submissions are generally due at the end of January, and the conference is generally held the following July. The first ICML was held 1980 in Pittsburgh.^[2]^[3]

Locations

ICML 2026 Seoul, South Korea
ICML 2025 Vancouver, Canada
ICML 2024 Vienna, Austria
ICML 2023 Honolulu, Hawaii, United States
ICML 2022 Baltimore, Maryland, United States
ICML 2021 Vienna, Austria (virtual conference)
ICML 2020 Vienna, Austria (virtual conference)
ICML 2019 Los Angeles, United States
ICML 2018 Stockholm, Sweden
ICML 2017 Sydney, Australia
ICML 2016 New York City, United States
ICML 2015 Lille, France
ICML 2014 Beijing, China
ICML 2013 Atlanta, United States
ICML 2012 Edinburgh, United Kingdom
ICML 2011 Bellevue, United States
ICML 2010 Haifa, Israel
ICML 2009 Montréal, Canada
ICML 2008 Helsinki, Finland
ICML 2007 Corvallis, Oregon, United States
ICML 2006 Pittsburgh, Pennsylvania, United States
ICML 2005 Bonn, Germany
ICML 2004 Banff, Canada
ICML 2003 Washington DC, United States
ICML 2002 Sydney, Australia
ICML 2001 Williamstown, Massachusetts, United States
ICML 2000 Stanford, California, United States
ICML 1999 Bled, Slovenia
ICML 1998 Madison, United States
ICML 1997 Nashville, United States
ICML 1996 Bari, Italy
ICML 1995 Tahoe City, United States
ICML 1994 New Brunswick, United States
ICML 1993 Amherst, United States
ICML 1992 Aberdeen, United Kingdom
ICML 1991 Evanston, United States
ICML 1990 Austin, United States
ICML 1989 Ithaca, United States
ICML 1988 Ann Arbor, United States
ICML 1987 Irvine, United States
ICML 1985 Skytop, United States
ICML 1983 Monticello, United States
ICML 1980 Pittsburgh, United States

References

External links

Differentiable computing

General

Concepts

Applications

Hardware

Software libraries

Implementations

Audio–visual	AlexNet WaveNet Human image synthesis HWR OCR Speech synthesis Speech recognition Facial recognition AlphaFold DALL-E Midjourney Stable Diffusion Text-to-video models Sora Whisper
Verbal	Word2vec Seq2seq BERT Gemini LaMDA Bard NMT Project Debater IBM Watson IBM Watsonx Granite GPT-1 GPT-2 GPT-3 GPT-4 ChatGPT GPT-J Chinchilla AI PaLM BLOOM LLaMA PanGu-Σ
Decisional	AlphaGo AlphaZero Q-learning SARSA OpenAI Five Self-driving car MuZero Action selection Auto-GPT Robot control