Kaggle: PLAsTiCC Astronomical Classification

29 сентября 2018 — 18 декабря 2018
Осталось 1 неделя
The Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) asks Kagglers to help prepare to classify the data from this new survey. Competitors will classify astronomical sources that vary with time into different classes, scaling from a small training set to a very large test set of the type the LSST will discover.

crowdAI: MARLO 2018

1 ноября 2018 — 20 декабря 2018
Осталось 1 неделя, 2 дня
Learning to Play: The Multi-Agent Reinforcement Learning in MalmO Competition (“Challenge”) is a new challenge that proposes research on Multi-Agent Reinforcement Learning using multiple games. Participants would create learning agents that will be able to play multiple 3D games as defined in the MalmO platform. The aim of the competition is to encourage AI research on more general approaches via multi-player games. For this, the Challenge will consist of not one but several games, each one of them with several tasks of varying difficulty and settings. Some of these tasks will be public and participants will be able to train on them. Others, however, will be private, only used to determine the final rankings of the competition.

crowdAI: AI-generated music challenge

1 января 2018 — 31 декабря 2018
Осталось 2 недели, 6 дней
In this challenge, participants are tasked to generate an AI model that learns on a large data set of music (in the form of MIDI files), and is then capable of producing its own music. Concretely, the model must produce a music piece in response to a short “seed” MIDI file that is given as input. There are two special aspects of this challenge, apart from the extremely interesting application. First, the results of the models will be evaluated by humans, with an ELO-style system where volunteers are given two randomly paired pieces of generated music, and choose the one they like better. Second, the top five models will at the end each generate a piece of music that will be performed live on stage at the Applied Machine Learning Days!

crowdAI: Spotify Sequential Skip Prediction Challenge

1 ноября 2018 — 31 декабря 2018
Осталось 2 недели, 6 дней
We release this dataset and challenge in the hope of spurring research on this important and understudied problem in streaming. Our challenge focuses on the task of session-based sequential skip prediction, i.e. predicting whether users will skip tracks, given their immediately preceding interactions in their listening session.

Kaggle: Predict Future Sales

8 февраля 2018 — 2 января 2019
Осталось 3 недели, 1 день
This challenge serves as final project for the "How to win a data science competition" Coursera course. In this competition you will work with a challenging time-series dataset consisting of daily sales data, kindly provided by one of the largest Russian software firms - 1C Company.

Kaggle: Two Sigma Using News to Predict Stock Movements

25 сентября 2018 — 9 января 2019
Осталось 4 недели, 1 день
By analyzing news data to predict stock prices, Kagglers have a unique opportunity to advance the state of research in understanding the predictive power of the news. This power, if harnessed, could help predict financial outcomes and generate significant economic impact all over the world.

Kaggle: Human Protein Atlas Image Classification

4 октября 2018 — 11 января 2019
Осталось 1 месяц
In this competition, Kagglers will develop models capable of classifying mixed patterns of proteins in microscope images. The Human Protein Atlas will use these models to build a tool integrated with their smart-microscopy system to identify a protein's location(s) from a high-throughput image.

Kaggle: Traveling Santa 2018 - Prime Paths

20 ноября 2018 — 11 января 2019
Осталось 1 месяц
This year, Rudolph believes he can motivate the overworked Reindeer team by wisely choosing the order in which they visit the houses on Santa's list. The houses in prime cities always leave carrots for the Reindeers alongside the usual cookies and milk. These carrots are just the sustenance the Reindeers need to keep pace. In fact, Rudolph has found that if the Reindeer team doesn't visit a prime city exactly every 10th step, it takes the 10% longer than it normally would to make their next destination! Can you help Rudolph solve the Traveling Santa problem subject to his carrot constraint? His team--and Santa--are counting on you!

Kaggle: PUBG Finish Placement Prediction

5 октября 2018 — 31 января 2019
Осталось 1 месяц, 3 недели
The team at PUBG has made official game data available for the public to explore and scavenge outside of "The Blue Circle." This competition is not an official or affiliated PUBG site - Kaggle collected data made possible through the PUBG Developer API. You are given over 65,000 games' worth of anonymized player data, split into training and testing sets, and asked to predict final placement from final in-game stats and initial player ratings. What's the best strategy to win in PUBG? Should you sit in one spot and hide your way into victory, or do you need to be the top shot? Let's let the data do the talking!

Kaggle: Quora Insincere Questions Classification

6 ноября 2018 — 6 февраля 2019
Осталось 1 месяц, 3 недели
In this competition, Kagglers will develop models that identify and flag insincere questions. To date, Quora has employed both machine learning and manual review to address this problem. With your help, they can develop more scalable methods to detect toxic and misleading content. Here's your chance to combat online trolls at scale. Help Quora uphold their policy of “Be Nice, Be Respectful” and continue to be a place for sharing and growing the world’s knowledge.

Kaggle: Histopathologic Cancer Detection

16 ноября 2018 — 1 апреля 2019
Осталось 3 месяца, 3 недели
In this competition, you must create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. The data for this competition is a slightly modified version of the PatchCamelyon (PCam) benchmark dataset (the original PCam dataset contains duplicate images due to its probabilistic sampling, however, the version presented on Kaggle does not contain duplicates).