• 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 and deep learning basics.
• Introduction to advanced neural network architectures (e.g., transformers, graph neural networks).
• Discussing recent research papers and breakthroughs in deep learning.

• Exploring advanced optimization techniques for deep learning (e.g., adaptive learning rates, second-order methods).
• Discussing recent advancements in optimization algorithms.
• Hands-on activity: Implementing state-of-the-art optimization techniques in deep learning frameworks.

• Deep dive into advanced reinforcement learning algorithms (e.g., deep Q-networks, policy gradients, meta-learning).
• Understanding recent research trends and challenges in reinforcement learning.
• Hands-on activity: Implementing advanced RL algorithms and experimenting with complex environments.

1. • Exploring advanced AI applications in robotics and autonomous systems.
   • Discussing recent research in robot perception, planning, and control.
   • Hands-on activity: Developing and testing advanced robotic algorithms in simulation or with real-world robots.

• Introduction to advanced natural language processing (NLP) techniques (e.g., transformer-based models, language generation).
• Understanding recent advancements in NLP research and applications.
• Hands-on activity: Fine-tuning transformer models for advanced NLP tasks such as language translation or question answering.

• Exploring ethical considerations in AI development and deployment.
• Discussing topics such as fairness, accountability, transparency, and bias in AI systems.
• Hands-on activity: Analyzing case studies and developing strategies for ethical AI design and deployment.

• Guided research project development.
• Providing mentorship and resources for students to work on advanced AI projects or conduct independent research.
• Hands-on activity: Developing research proposals and project plans for advanced AI projects.

• Final project presentations and peer review.
• Reflecting on the research process, findings, and implications.
• 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.
• Publication and Conference Participation: Supporting students in submitting research papers or presenting their findings at conferences, workshops, or online forums.
• Internship Opportunities: Facilitating internships or research internships at AI companies or research institutions for interested students.
• Advanced Reading Groups: Organizing reading groups/journal clubs to discuss recent research papers and developments in AI.

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