June 22nd – 26th, 2026 | 10am - 3pm each day | $850
Students are encouraged to attend in person in Cupertino CA but virtual attendance is also available
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In this hands-on summer course, students will learn how modern Artificial Intelligence and Machine Learning systems work and then use those skills to build an original AI project based on their own interests.
We’ll begin by exploring the core ideas behind AI through interactive lessons and a guided project, then transition into independent project development with personalized support and mentorship throughout the process. Whether students are interested in sports, music, games, finance, politics, public health, social media, art, or science, they’ll have the opportunity to create something meaningful and uniquely their own.
Course objectives:
By the end of the week, students should:
Topics may include:
Machine Learning fundamentals
Regression & prediction models
Classification systems
Gradient Descent & Stochastic Gradient Descent
Regularization
Neural Networks & Deep Learning
Image recognition & computer vision
K-Nearest Neighbors (KNN)
Semantic embeddings
Natural language processing
Modern generative AI systems
Specific topics may adapt based on student interests and experience levels.
Projects planned:
Project 1 Linear Regression: This is a guided introduction to ML that will take place in the afternoon of Day 1. Real-world data will be provided, and students will use the concepts taught in the Day 1 morning lecture to create a regression model for housing data.
Project 2 Supervised Learning: In this project, students will have the freedom to choose their own topic and dataset (within a set of guidelines from the instructor). The instructor will guide students as they create a prediction or classification model.
Project 3 Natural Language Processing: Students will create a more advanced project using NLP to analyze or generate text. In this project, students will have the greatest degree of freedom to choose the style/purpose/topic of the project.
Students will work on projects during class time with guidance from the instructor
Prerequisites:
Students should:
Have completed Algebra I
Be comfortable with basic coding concepts
(for example: modifying elements in an array/list using a loop)
AI and Machine Learning rely on advanced mathematical ideas such as Linear Algebra and Calculus, so we will use those concepts in the course, but students are NOT expected to have completed those courses beforehand. All necessary concepts will be introduced intuitively and explained throughout the class.
When: June 22nd – 26th, 2026 | 10am - 3pm each day