syllabus

The International Olympiad in Artificial Intelligence (IOAI) is a premier global competition for high school students, aiming to cultivate both a strong theoretical foundation and hands-on expertise in Artificial Intelligence.

Topic Classifications

The topics are categorized into three distinct sections:

  • Theory: Understanding core concepts and theoretical underpinnings.
  • Practice: Developing the practical skills necessary to implement AI methods.
  • Both: Topics that require knowledge of both theoretical principles and practical application.

Section 1: Foundational Skills & Classical Machine Learning

Programming Fundamentals

  • Python Basics (Loops, Functions, etc.) - Practice
  • NumPy and Pandas for Data Handling - Practice
  • Matplotlib and Seaborn for Visualization - Practice
  • Scikit-learn for ML - Practice
  • PyTorch Basics - Practice
  • Tensor Manipulation - Practice

Supervised Learning

  • Linear Regression - Both
  • Logistic Regression - Both
  • K-Nearest Neighbors (K-NN) - Both
  • Decision Trees - Both
  • Random Forests - Practice

Section 2: Neural Networks & Deep Learning

Neural Networks

  • Perceptron Basics - Both
  • Gradient Descent - Both
  • Backpropagation - Both

Section 3: Computer Vision

  • Fundamentals of Convolutional Layers - Both
  • Pooling Techniques (Max, Average) - Both
  • Basic Image Classification - Both

Section 4: Natural Language Processing

  • Word Embeddings (Word2Vec, GloVe) - Practice
  • Transformers Basics (Attention Mechanism) - Both
  • Introduction to Pre-trained NLP Models (e.g., BERT, GPT) - Practice