Course Overview

Welcome to USAAIO Foundations – Machine Learning & Data Analysis (Part 2 / Semester 2).

This course is the first semester of our year-long USAAIO Foundations program. Part 1 and Part 2 together form one complete curriculum, providing students with a structured path from mathematical foundations and classical machine learning to advanced data analysis and AI competition problem solving.

For convenience and flexibility, the program is offered in two semesters. The curriculum below represents the complete USAAIO Foundations syllabus, with the core mathematical concepts and introductory machine learning covered in Part 1, followed by advanced algorithms, model optimization, and competition applications in Part 2.

Complete Curriculum

Throughout the full program, students will learn:

• Mathematics for Machine Learning – Basic Linear algebra, vectors, matrices, probability, and statistics
• Data Analysis – Exploring, visualizing, and interpreting datasets
• Data Preprocessing – Cleaning, transforming, and preparing data for machine learning
• Supervised Learning – Regression and classification algorithms
• Unsupervised Learning – Clustering and dimensionality reduction techniques
• Model Evaluation – Accuracy, precision, recall, F1 score, ROC curves, and cross-validation
• Feature Engineering – Selecting and constructing meaningful features
• Model Optimization – Hyperparameter tuning and performance improvement
• Ensemble Methods – Combining multiple models for better predictions
• Competition Problem Solving – Applying machine learning techniques to USAAIO-style challenges
• Python for Machine Learning – Using Python libraries to build and evaluate machine learning models
• Machine Learning Reasoning – Understanding why algorithms work and selecting appropriate models

Course Features

• Hands-on machine learning projects using real-world datasets
• Guided walkthroughs with mathematical intuition and algorithm explanations
• Weekly instructor support, Q&A, and model review sessions
• Recorded lessons, notes, and complete Python examples for flexible learning
• AI powered Homework/ladder system

Ideal For

• Students preparing for the USAAIO (USA Artificial Intelligence Olympiad)
• Middle and high school students interested in AI and machine learning competitions
• Students with basic Python programming experience who want to learn machine learning
• Learners planning to advance into AI competitions or future deep learning studies

Duration

Each semester runs for 12–16 weeks, with one session per week (1–2 hours per session).

Prerequisites

Students should be familiar with:

• Basic Python programming
• Basic algebra
• Introductory statistics (helpful but not required)

No prior machine learning experience is required.

Materials Included

• Video lessons and annotated machine learning walkthroughs
• Weekly homework and challenge problems
• Python notebooks and reusable code templates
• Practice datasets and step-by-step data analysis exercises

Outcome

Students who complete both Part 1 and Part 2 will have covered the full USAAIO Foundations curriculum and will possess a strong understanding of classical machine learning, data analysis, and the mathematical reasoning required for USAAIO and future AI competition success.

Join us and build a strong foundation in machine learning and artificial intelligence!