• Start in Elementary School – no math or coding pre-requisite!

• Learn what AI is, and how to build AI and use it

• Self paced projects, videos and online exercises to continue your learning outside of class

• Learn how AI works, from AI experts

• Learn how things in your daily life (like Alexa, phones, and self driving cars) use AI

• Build your own AI and teach it how to play games and chat with you!

• Start in Elementary School – no need to know math or coding

• Courses that advance with you to Middle School, High School, competitions and beyond. Lots of ways to use coding and AI as you learn to code!

• Build cool projects to showcase your skills to parents, teachers and friends!

• Review of neural networks, deep learning, and common architectures.
• Introduction to advanced topics in deep learning (e.g., autoencoders, recurrent neural networks).
• Discussing applications such as speech recognition and time series analysis.

• Exploring advanced optimization techniques for training deep neural networks.
• Discussing optimization algorithms like Adam, RMSprop, and learning rate schedules.
• Hands-on activity: Implementing advanced optimization techniques in training neural networks.

• Introduction to advanced deep learning concepts (e.g., variational autoencoders, deep reinforcement learning).
• Understanding the principles behind variational inference and probabilistic modeling.
• Hands-on activity: Implementing a variational autoencoder for image generation or anomaly detection.

• Deep dive into deep reinforcement learning algorithms (e.g., deep Q-networks, policy gradients).
• Discussing recent advancements and challenges in reinforcement learning research.
• Hands-on activity: Implementing a deep reinforcement learning algorithm to solve a simulated control task.

• Exploring advanced natural language processing (NLP) techniques (e.g., transformer models, BERT).
• Understanding pretraining and fine-tuning strategies for NLP tasks.
• Hands-on activity: Fine-tuning a pretrained transformer model for text classification or question answering.

• Discussing advanced AI applications in areas such as healthcare, finance, and autonomous systems.
• Exploring case studies and real-world examples of AI applications in diverse domains.
• Hands-on activity: Analyzing and discussing the societal impact and ethical considerations of AI technologies.

• Guided project development and research exploration.
• Providing resources and mentorship for students to work on advanced AI projects or conduct independent research.
• Hands-on activity: Developing and refining AI projects based on students’ interests and research goals.

• Final project presentations and peer review.
• Reflecting on the research process, findings, and lessons learned.
• Discussing potential future directions in AI research and career pathways in the field.

• Research Mentorship: Pairing students with mentors from academia or industry to guide them through research projects and provide feedback.
• Internship Opportunities: Facilitating internships or shadowing experiences at AI companies or research labs for interested students.
• Publication and Conference Participation: Supporting students in submitting research papers or presenting their findings at conferences, workshops, or online forums.

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