RAIF-Challenge 2017

24 сентября 2017 — 25 октября 2017
Осталось 5 дней, 3 часа
Задачи: AI в банках, AI в страховании, AI в ритейле

NIPS 2017: Learning to Run

20 июня 2017 — 30 октября 2017
Осталось 1 неделя, 3 дня
Your task is to build a function f which takes the current state observation (a 41 dimensional vector) and returns the muscle excitations action (18 dimensional vector) in a way that maximizes the reward. Your total reward is the position of the pelvis on the x axis after the last iteration minus a penalty for using ligament forces. Ligaments are tissues which prevent your joints from bending too much - overusing these tissues leads to injuries, so we want to avoid it. The penalty in the total reward is equal to the sum of forces generated by ligaments over the trial, divided by 1000. For details on evaluation please refer to the Getting Started guide in the Dataset section of the challenge.

Sberbank Data Science Journey

24 сентября 2017 — 30 октября 2017
Осталось 1 неделя, 3 дня
В этом году две задачи: определение релевантности вопроса и построение вопрос-ответной системы.

NIPS 2017: The Conversational Intelligence Challenge

4 апреля 2017 — 12 ноября 2017
Осталось 3 недели, 2 дня
Dialogue systems and conversational agents – including chatbots, personal assistants and voice control interfaces – are becoming increasingly widespread in our daily lives. In addition to the growing real-world applications, the ability to converse is also closely related to the overall goal of AI. Recent advances in machine learning have sparked a renewed interest for dialogue systems in the research community. This NIPS Live Competition aims to unify the community around the challenging task: building systems capable of intelligent conversations. Teams are expected to submit dialogue systems able to carry out intelligent and natural conversations of news articles with humans. At the final stage of the competition participants, as well as volunteers, will be randomly matched with a bot or a human to chat and evaluate answers of a peer. We expect the competition to have two major outcomes: (1) a measure of quality of state-of-the-art dialogue systems, and (2) an open-source dataset collected from evaluated dialogues.

Multimodal Emotion Recognition Challenge

14 октября 2017 — 13 ноября 2017
Осталось 3 недели, 3 дня
Most of studies on emotion recognition problem are focused on single-channel recognition or multimodal approaches when the data is available for the whole dataset. However, in some practical cases data sources could be missed, noised or broken. Here we present you with the first machine learning competition on multimodal emotion recognition with missing data. The main goal of this challenge is to find approaches for a reliable recognition of emotional behavior when some data is unavailable. Your task will be to predict one of the six basic emotions (happiness, sadness, anger, disgust, fear and neurtal state) based on the dataset of emotions acted by semi-professionals. You will be presented with features for 4 modalities: audio, facial expressions, body-motion and eye-tracking. You need to beat the baseline solution based on naïve approach to compete for the prizes.

Kaggle: Text Normalization Challenge - Russian Language

5 сентября 2017 — 15 ноября 2017
Осталось 3 недели, 5 дней
In this competition, you are challenged to automate the process of developing text normalization grammars via machine learning. This track will focus on Russian, while a separate will focus on English here: English Text Normalization Challenge

The Alzheimer's Disease Prediction Of Longitudinal Evolution Challenge

15 июня 2017 — 15 ноября 2017
Осталось 3 недели, 5 дней
TADPOLE challenges you to identify what data and algorithms best predict AD progression. This facilitates early identification of patients likely to be receptive to treatment, thus purifying cohorts for clinical trials, highlighting positive treatment effects, and facilitating translation of effective treatments to market.

Kaggle: Text Normalization Challenge - English Language

5 сентября 2017 — 15 ноября 2017
Осталось 3 недели, 5 дней
In this competition, you are challenged to automate the process of developing text normalization grammars via machine learning. This track will focus on English, while a separate will focus on Russian here: Russian Text Normalization Challenge

Kaggle: Porto Seguro’s Safe Driver Prediction

30 сентября 2017 — 30 ноября 2017
Осталось 1 месяц, 1 неделя
In this competition, you’re challenged to build a model that predicts the probability that a driver will initiate an auto insurance claim in the next year. While Porto Seguro has used machine learning for the past 20 years, they’re looking to Kaggle’s machine learning community to explore new, more powerful methods. A more accurate prediction will allow them to further tailor their prices, and hopefully make auto insurance coverage more accessible to more drivers.

Прогнозирование вероятности невозврата кредита

2 августа 2017 — 30 ноября 2017
Осталось 1 месяц, 1 неделя
Целью конкурса является разработка модели кредитного скоринга, основанная на анализе данных о кредитном поведении клиентов, полученных из внешних источников. Данные представляют собой кредитные истории: атрибуты кредитов, выданных клиенту в прошлом. Данные поступают из 4-х различных источников, идентификатор источника указан в поле NUM_SOURCE. Задача усложняется тем, что данные могут быть неполны: некоторые источники могут не содержать информацию о части выданных кредитов, противоречивы – источники могут содержать разные значения атрибутов по одному и тому же кредиту, возможны коллизии других типов. В качестве первого этапа решения задачи конкурса участникам предлагается предложить способ объединения данных нескольких источников и поиска и фильтрации выбросов.

Topcoder: IAPRA The functional Map of the World Challenge

1 августа 2017 — 15 декабря 2017
Осталось 1 месяц, 3 недели
The functional Map of the World (fMoW) Challenge invites solvers from around the world to develop deep learning and other automated techniques to classify points of interest from satellite imagery. The goal of the challenge is to facilitate breakthroughs in object identification and classification to automatically identify facility, building, and land use.

Kaggle: Cdiscount’s Image Classification Challenge

14 сентября 2017 — 15 декабря 2017
Осталось 1 месяц, 3 недели
In this challenge you will be building a model that automatically classifies the products based on their images. As a quick tour of Cdiscount.com's website can confirm, one product can have one or several images. The data set Cdiscount.com is making available is unique and characterized by superlative numbers in several ways: - Almost 9 million products: half of the current catalogue - More than 15 million images at 180x180 resolution - More than 5000 categories: yes this is quite an extreme multi-class classification!

Kaggle: Passenger Screening Algorithm Challenge

22 июня 2017 — 16 декабря 2017
Осталось 1 месяц, 3 недели
Currently, TSA purchases updated algorithms exclusively from the manufacturers of the scanning equipment used. These algorithms are proprietary, expensive, and often released in long cycles. In this competition, TSA is stepping outside their established procurement process and is challenging the broader data science community to help improve the accuracy of their threat prediction algorithms. Using a dataset of images collected on the latest generation of scanners, participants are challenged to identify the presence of simulated threats under a variety of object types, clothing types, and body types. Even a modest decrease in false alarms will help TSA significantly improve the passenger experience while maintaining high levels of security.

Kaggle: WSDM - KKBox's Churn Prediction Challenge

19 сентября 2017 — 18 декабря 2017
Осталось 1 месяц, 4 недели
In this competition you’re tasked to build an algorithm that predicts whether a user will churn after their subscription expires. Currently, the company uses survival analysis techniques to determine the residual membership life time for each subscriber. By adopting different methods, KKBOX anticipates they’ll discover new insights to why users leave so they can be proactive in keeping users dancing. Winners will present their findings at the WSDM conference February 6-8, 2018 in Los Angeles, CA. For more information on the conference, click here.

Kaggle: WSDM - KKBox's Music Recommendation Challeng

28 сентября 2017 — 18 декабря 2017
Осталось 1 месяц, 4 недели
WSDM has challenged the Kaggle ML community to help solve these problems and build a better music recommendation system. The dataset is from KKBOX, Asia’s leading music streaming service, holding the world’s most comprehensive Asia-Pop music library with over 30 million tracks. They currently use a collaborative filtering based algorithm with matrix factorization and word embedding in their recommendation system but believe new techniques could lead to better results.

DrivenData: DengAI Predicting Disease Spread

1 января 2017 — 22 декабря 2017
Осталось 2 месяца
Accurate dengue predictions would help public health workers ... and people around the world take steps to reduce the impact of these epidemics. But predicting dengue is a hefty task that calls for the consolidation of different data sets on disease incidence, weather, and the environment.

Kaggle: Zillow’s Home Value Prediction (Zestimate)

24 мая 2017 — 17 января 2018
Осталось 2 месяца, 4 недели
Zillow’s Zestimate home valuation has shaken up the U.S. real estate industry since first released 11 years ago. A home is often the largest and most expensive purchase a person makes in his or her lifetime. Ensuring homeowners have a trusted way to monitor this asset is incredibly important. The Zestimate was created to give consumers as much information as possible about homes and the housing market, marking the first time consumers had access to this type of home value information at no cost.

DrivenData: Concept to Clinic

4 августа 2017 — 25 января 2018
Осталось 3 месяца, 1 неделя
There is a daunting chasm between research algorithms and clinical practice. We want to bridge this gap by developing an end-to-end application, as a community, that connects the predictive power of machine learning with functional software tested against errors and a clean user interface focused on clinical use.

NLP Challenges for Detecting Medication and Adverse Drug Events from Electronic Health Records

1 ноября 2017 — 1 февраля 2018
Осталось 3 месяца, 2 недели
Adverse drug events (ADEs) are common and occur in approximately 2-5% of hospitalized adult patients. Each ADE is estimated to increase healthcare cost by more than $3,200. Severe ADEs rank among the top 5 or 6 leading causes of death in the United States. Prevention, early detection and mitigation of ADEs could save both lives and dollars. Employing natural language processing (NLP) techniques on electronic health records (EHRs) provides an effective way of real-time pharmacovigilance and drug safety surveillance. We’ve annotated 1092 EHR notes with medications, as well as relations to their corresponding attributes, indications and adverse events. It provides valuable resources to develop NLP systems to automatically detect those clinically important entities. Therefore we are happy to announce a public NLP challenge, MADE1.0, aiming to promote deep innovations in related research tasks, and bring researchers and professionals together exchanging research ideas and sharing expertise. The ultimate goal is to further advance ADE detection techniques to improve patient safety and health care quality.