June 22nd – 26th, 2026 | 10am - 3pm each day | $850

Registration: https://youtu.be/ciWRPMaVXS4

Students are encouraged to attend in person in Cupertino, CA, but virtual attendance is also available

 

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:

• Be able to confidently talk about AI theory/concepts in internship interviews, school clubs, research opportunities, etc
• Have completed multiple projects which they can write about for college applications or discuss in internship interviews

 

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.

 

About the instructor:

Ian Davoren holds a Master’s degree in Computer Science from New York University, where he focused on machine learning and artificial intelligence. He has developed numerous AI-driven software projects and conducted data science research. Ian has extensive experience teaching and mentoring students and has worked with Springlight since 2018. He is passionate about helping students learn how AI works and build their own projects.