Educational Resources
Freely Available Educational Resources
A collection of open educational materials, national Olympiad problems, and online courses to help students prepare for the International Olympiad in Artificial Intelligence (IOAI).
IOAI Syllabus and Tasks
- Official IOAI Syllabus – Full outline of topics and competencies covered in IOAI exams, including Python, machine learning, AI ethics, and data analysis.
- IOAI 2025 Tasks Repository – Public repository of problems, datasets, and example solutions from the 2025 competition.
- IOAI 2024 Tasks Repository – Archive of 2024 tasks and resources for training and benchmarking.
National and Regional Olympiads in AI
Explore national and regional competitions that serve as official IOAI qualifiers or independent training events. Each includes datasets, tasks, and baseline code open to the public.
Asia-Pacific Olympiad in AI (APOAI)
- APOAI 2025 Competition – Overview of tasks and participation details for the 2025 regional event.
Northern Eurasia Olympiad in AI (NEOAI 2025)
- Evading AI-Generated Text Detection – NLP – craft text that bypasses AI detectors.
- Broken BERT – NLP – repair corrupted BERT embeddings.
- Cluster Pictures – CV – cluster image patches without labels.
- Cuties Segmentation – CV – segment cartoon characters.
- Hogspell Challenge – Generative Image – steer a diffusion model to generate hogs.
- Tricy Table – ML – tabular data feature engineering.
- Underfitting CV – CV – improve underfitting models.
- Intent Detection & Slot Filling – NLP – multilingual intent recognition.
- Overview: NEOAI 2025 on Kaggle
Benin – National AI Olympiad Selection 2025
- Kaggle competition – ML – binary classification challenge.
Bangladesh – BdAIO 2025
- Preliminary Round – Algorithms – 11 AI-themed programming problems.
China – NOAI 2024
- Basketball Shooting – Tabular – predict shot success.
- News Text Classification – NLP – categorize news.
- Pendulum Motion – Time Series – model missing data.
- Real or Fake Image – CV – detect generated images.
China – NOAI 2025
- Phase 1 Qualifier – Written test based on IOAI syllabus.
- Phase 2 Finals – Four advanced AI tasks across ML, CV, NLP, and ethics.
Georgia – National Selection 2025
India – INAIO 2025
- Official Website – National selection details and resources.
Japan – JOAI 2025
- Kaggle competition – Multimodal – classify gases using sensors, images, and text.
Kazakhstan – Team Selection 2025
- Day 1: Image Restoration – CV – restore filtered images.
- Day 2: Player Clustering – ML – cluster football players.
- Day 3: Code Difficulty – NLP – classify code difficulty.
- Day 4: Masked Word Position – NLP – recover masked word position.
- All Solutions – Consolidated repository.
Malaysia – IOAI Training Programme 2025
- Lab Repository – ML/CV – ResNet fine-tuning, FCN segmentation, multimodal fusion.
Poland – Polish AI Olympiad
- 1st Edition (2024) – CV/ML/NLP – adversarial attacks, quantization, time-series, translation.
- 2nd Edition (2025) – CV/ML/NLP – coin detection, hallucination detection, OOD classification.
Romania – ONIA 2025
- Example Problems – Tabular ML samples.
- Training Camp Overview – Multi-round national selection.
- Solutions – Write-ups for ROAI challenges.
Serbia – National Selection 2025
- Overview – National finals with research and practical components.
Tunisia – National AI Olympiad 2025
- Overview – Hackathon-style AI competition selecting IOAI representatives.
United States – USA-North America AI Olympiad 2025
For a more detailed list, you can visit: https://github.com/open-cu/awesome-ioai-tasks
Open Educational Materials
Freely accessible learning resources to build core knowledge in AI, ML, and data science.
Online Courses
- Fast.ai Practical Deep Learning – Hands-on course in PyTorch for deep learning applications.
- Google Machine Learning Crash Course – Interactive ML tutorials with Colab notebooks.
- MIT 6.S191 – Introduction to Deep Learning – University-level deep learning fundamentals.
- Elements of AI – Beginner-friendly introduction to key AI concepts.
E-Books and Lecture Notes
- Dive into Deep Learning – Free interactive textbook with Jupyter notebooks.
- Deep Learning Book (Goodfellow et al.) – Comprehensive theoretical foundation.
- Machine Learning Yearning (Andrew Ng) – Practical guidance for ML project design.
- Deep Learning with Python, Third Edition – Practical guide into Deep Learning.
Jupyter Notebooks and Slides
- Kaggle Learn – Short, notebook-based ML lessons and projects.
- PyTorch Tutorials – Example notebooks for model training and deployment.
- Stanford CS231n Slides – Computer Vision and CNN lecture slides.
- Stanford CS224n Slides – NLP and transformer-based model course materials.
For a more comprehensive list of freely available resources, please visit here.
