What this site covers
Each module is a self-contained page. The study plan stitches them into a weekly progression from math foundations through transformer fine-tuning.
About the contest
Format, eligibility, the two-round structure, in-person final, Team USA selection for IOAI / IAIO.
Math you need
Linear algebra, probability and statistics, multivariable calculus, convex optimization — only the parts that actually show up in ML.
Python data stack
NumPy, pandas, matplotlib, seaborn, scikit-learn — environment setup, idioms, common pitfalls.
Classical ML
Regression, classification, ensembles, cross-validation, clustering, dimensionality reduction — the scikit-learn surface.
PyTorch & neural nets
Tensors, autograd, MLPs, the standard layers, forward/backpropagation, training loops, regularization.
Attention & transformers
Tokenization, embeddings, self-attention, transformer blocks, pre-training and fine-tuning, NLP and vision applications.
Six-month plan
Week-by-week schedule from math review through deep learning fluency, calibrated for a Grade 9 ramp.
Resources
Textbooks, courses, datasets, paper recommendations, and competitive practice grounds (Kaggle, AIcrowd).
Suggested reading order
- Get oriented. Read About the contest to understand the two-round format and what the in-person final actually looks like.
- Lock in the math. Work through the math review. You don't need all of multivariable calculus — just the slice that powers gradient descent and PCA.
- Get fluent in the Python data stack. The Python toolkit covers NumPy + pandas + matplotlib until you can manipulate arrays without thinking.
- Sweep classical ML. The classical ML page covers every scikit-learn family in one sitting: linear models, trees, ensembles, clustering.
- Build a neural net from scratch. The deep learning page walks through a manual MLP in NumPy, then the same thing in PyTorch.
- Understand attention. The transformers page goes from tokenizer to scaled dot-product attention to a working transformer block.
Why an AI olympiad is different
- Theory and code are both graded. Pure math kids who can't ship a notebook fail. Pure coders who can't reason about gradients fail. You need both.
- Datasets are part of the problem. Loading, cleaning, splitting, and feature engineering carry as many points as model choice.
- Compute is constrained. Final-round problems often run on a fixed CPU budget — a 100M-parameter transformer isn't the right answer; a 200K-parameter MLP often is.
- Reproducibility matters. Random seeds, deterministic training, and version pinning are part of the deliverable.