• 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 concepts.
• Introduction to advanced neural network architectures (e.g., recurrent neural networks, long short-term memory networks).
• Discussing applications such as sequence prediction and language modeling.

• Understanding attention mechanisms in neural networks.
• Exploring transformer architectures for natural language processing tasks.
• Hands-on activity: Implementing a simple transformer model for text generation or translation.

• Introduction to generative adversarial networks (GANs).
• Exploring the concept of generative modeling and adversarial training.
• Hands-on activity: Building a basic GAN model to generate images.

• Understanding transfer learning and fine-tuning pretrained models.
• Discussing the advantages and limitations of transfer learning.
• Hands-on activity: Fine-tuning a pretrained deep learning model for a specific task (e.g., image classification).

• Deep dive into reinforcement learning algorithms (e.g., deep Q-networks, policy gradients).
• Understanding exploration-exploitation trade-off and reward shaping.
• Hands-on activity: Implementing a reinforcement learning algorithm to train an agent for a simulated environment.

6. Session 6 (2 hours):
• Exploring AI applications in robotics.
• Discussing challenges and opportunities in robot perception, control, and planning.
• Hands-on activity: Programming a simulated robot to navigate and perform tasks in a virtual environment.

• Exploring cutting-edge AI research topics (e.g., meta-learning, self-supervised learning).
• Discussing recent advancements and breakthroughs in AI technology.
• Hands-on activity: Working on advanced AI projects or exploring research papers in the field.

• Project presentations and peer review.
• Reflecting on the challenges, learnings, and achievements throughout the course.
• Discussing future directions in AI research and potential career paths in the field.

• Industry Collaboration: Guest lectures, workshops and internship opportunities for interested students.
• Research Projects: Students to explore and conduct independent research projects on AI topics of their choice, with guidance from teachers and mentors.
• Publications and Conferences: Opportunities for students to present their research findings or AI projects at school exhibitions, conferences, or online platforms.

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