ML Olympiad is back with more than 20 challenges

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The popular ML Olympiad is back for its third round with over 20 community-hosted machine learning competitions on Kaggle.

ML Olympiad – organized by groups including ML GDE, TFUG, and other ML communities – aims to provide developers with opportunities to learn and practice machine learning skills by tackling real-world challenges. Is.

In the last two rounds, an impressive 605 teams participated in 32 competitions, producing 105 discussions and 170 notebooks.

This year’s lineup includes challenges spanning fields such as healthcare, sustainability, natural language processing (NLP), computer vision, and more. Competitions are hosted by expert groups and developers from around the world.
This year’s challenges are:

  • Detection of smoking in patients

Hosted by Rishiraj Acharya (AI/ML GDE) in collaboration with TFUG Kolkata, the competition tasks participants with predicting smoking status using biosignal ML models.

Organized by Anas Ludhiri under MLACT, the challenge calls for developing a classification model to distinguish between jellyfish and plastic pollution in marine imagery.

  • Detect hallucinations in LLMs.

Luca Massaron (AI/ML GDE) presents the unique challenge of identifying deception in the responses provided by the Mistral 7B instructor model.

Anushka Raj, with TFUG Hajipur, explores ML solutions to reduce food wastage, a critical concern in today’s world.

Organized by Ankit Kumar Verma and TFUG Prayagraj, the competition involved predicting body fat percentage in men using multiple regression methods.

Ayush Morbar of Offbeats Byte Labs invited participants to build a regression model to estimate the age of crabs.

TFUG Nashik challenges participants to predict weather conditions in Nashik, India by leveraging machine learning techniques.

  • Prediction of earthquake damage

Usha Rengraju presents the task of predicting the level of damage caused to buildings by earthquakes based on various factors.

  • Bangladesh weather forecast

TFUG Bangladesh (Dhaka) aims to forecast rainfall, average temperature, and rainy days for a particular day in Bangladesh.

  • The challenge of forecasting CO2 emissions

Mohammad Shehryar Azad Awan and Shuro Pal from TFUG North Bengal want to forecast per capita CO2 emissions for 2030 using global development indicators.

Kwan Hong (AI/ML GDE) challenges participants to predict the loan approval situation by addressing an important aspect of financial inclusion.

Ashwin Raj and participants in the BeyondML task to predict the liveability scores of properties promoting sustainable urban development.

  • Detecting Toxic Language (PTBR)

Hosted in Brazilian Portuguese, this challenge by Macairi Ohana, Pedro Gengo, and Vinicius F. Carrida (AI/ML GDE) involves ranking toxic tweets.

  • Improving disaster response

Yara Armal Desire of TFUG Abidjan invited participants to forecast humanitarian aid contributions in response to disasters around the world.

Kartiki Rawat from TFUG Durg called for developing predictive models to predict traffic density in urban areas.

  • Know your customer’s opinion.

TFUG Surabaya presents a challenge to classify user opinions into Likert scale categories.

  • India weather forecast

Mohammad Moinuddin and TFUG Hyderabad task participants predict temperatures for specific months in India.

Organized by TFUG Bhopal, the competition involves developing classification models to predict tumor malignancy.

  • AI powered job description generator

Akash Tripathi from TFUG Ghaziabad challenges participants to build a system that automatically generates job details using generative AI and a chatbot interface.

  • Machine translation French-Wolof

GalsenAI presents the challenge of accurately translating French sentences into Wolof, offering a platform to enhance language translation capabilities.

  • Water mapping using satellite imagery

ML Nomads engages participants with water mapping using satellite imagery to detect drought in Taha Bohsin Dam.

Google is helping every community hosting this round through its Google for Developers program.

Participants are encouraged to search for “ML Olympiad” on Kaggle, follow #MLOlympiad on social media, and enter the contests that interest them most.

With such a diverse array of real-world machine learning challenges, the ML Olympiad represents a great opportunity for developers to test their skills and gain valuable experience.

(Image credit: Google)

See also: Microsoft: China plans to disrupt elections with AI-generated disinformation

ML Olympiad is back with more than 20 challenges 2

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Tags: ai, artificial intelligence, challenge, competition, developers, development, machine learning, ml olympiad

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