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Learning causal relationships (WCCI 2008 and NIPS 2008)
What affects your health? What affects the economy? What affects climate changes? and… which actions will have beneficial effects? This series of competitions challenged the participants to discover the causes of given effects, based on observational data. The datasets include re-simulation data from models closely resembling real systems and real data for which the causal dependencies are known from experimental evidence. A first challenge on "causation and prediction" featuring 4 datasets (Genomics, Pharmacology, and Census data) was followed by a "pot-luck challenge" in which the participants exchanged tasks. Fifteen datasets are available to study causal problems.
ChaLearn Looking at People (ECCV 2014)
Three tracks of challenging computer vision tasks promising to advance how machines look at people:
Track 1: Human Pose Recovery.
Track 2: Action/Interaction Recognition
Track 3: Gesture Recognition.
ChaLearn Fast Causation Coefficient (MS Faculty Summit 2014)
Similar to the cause-effect pairs challenge, but this time, you get to submit code to the challenge platform. Your challenge is to build a fast causation coefficient. The proceedings are shared with the cause-effect paris challenge.
The ATLAS experiment has recently observed a signal of the Higgs boson decaying into two tau particles, but this decay is a small signal buried in background noise.
The goal of the Higgs Boson Machine Learning Challenge is to explore the potential of advanced machine learning methods to improve the discovery significance of the experiment.
AutoML challenge (IJCNN 2015)
The goal of the AutoML challenge is to create a machine capable of learning from examples without any human intervention. This challenge is concerned with regression and classification problems (binary, multi-class, or multi-label) from data already formatted in fixed-length feature-vector representations. The domains include biology and medicine, ecology, energy and sustainability management, image, text, audio, speech, video and other sensor data processing, internet social media management and advertising, market analysis and financial prediction.
Search challenge-related websites by typing keywords in the field above.
Data mining competitions:
A list of data mining competitions maintained by KDnuggets, including the well known KDD cup.
Platforms hosting/posting challenges:
Kaggle: The most popular hosting platform.
Tunedit: Similar platform more academically oriented (phased out?).
DrivenData: For non-profit challenges.
Codalab: For academic challenges of greater complexity.
Beat: A EU sponsored platform.
Epidemium: challenges in epidemiology.
Pascal challenges: The Pascal network is sponsoring several challenges in Machine learning.
Challenges.gov: Challenges sponsored by the US Government.
OpenML: share ML reusable frameworks.
MLcomp: compare machine learning programs.
E-lico: data mining portal.
H20: open source predictive analytics platform.
KNIME: Data mining platform.
Quantopian: Financial data simulator + ML tutorials.
Amazon Mechanical Turk: Gets you hire people from all around the world to solve your tasks. Used to label computer vision data.
Crowdflower: Hire people to collect, filter and enhance data.
International conferences hosting challenges:
WCCI: World congress on computational intelligence.
ICDAR: International Conference on Document Analysis and Recognition, a bi-annual conference proposing a contest in printed text recognition. Feature extraction/selection is a key component to win such a contest.
ICPR: In conjunction with the International Conference on Pattern Recognition, ICPR 2004, a face recognition contest is being organized.
ICMI: Competitions on multimodal interaction
NNGC: Neural Network Grand Challenge in time series forecasting.
Netflix: The 1 million dollar Netflix prize, which attracted a lot of attention and broke new grounds for recommender systems.
Robocup: Robots who play soccer, a yearly held contest.
DELVE: A platform developed at University of Torontoto benchmark machine learning algorithms.
CAMDA: Critical Assessment of Microarray Data Analysis, an annual conference on gene expression microarray data analysis. This conference includes a context with emphasis on gene selection, a special case of feature selection.
TREC: Text Retrieval conference, organized every year by NIST. The conference is organized around the result of a competition. Past winners have had to address feature extraction/selection effectively.
CASP: An important competition in protein structure prediction called Critical Assessment of Techniques for Protein Structure Prediction.
ICAPS competitions: Competitions in planning and knowledge engineering
MEDIAEVAL benchmarks: Benchmarking Initiative for Multimedia Evaluation. Data sharing in multimediacommons (with incremental annotations). Uses Amazon web services to allow experimentation in the cloud.
DREAM: Dialogue for Reverse Engineering Assessments and Methods. Challenges in gene network reconstruction.
Computer vision datasets
UCI machine learning repository: A great collection of datasets for machine learning research.
KEEL: Knowledge Extraction based on Evolutionary Learning.
Amazon datasets: Public datasets hosted by Amazon.