Students engage with AI at a near-professional level — working with advanced techniques, conducting independent research, engaging with primary sources, and developing informed positions on AI governance.
Students explore supervised and unsupervised learning at an advanced level, working with real datasets and implementing algorithms from scratch. They learn to evaluate models rigorously, handle imbalanced data, and understand the mathematical foundations of ML.
Students explore neural networks, convolutional networks for computer vision, and generative models. They build models using TensorFlow/PyTorch, train on GPUs, and explore state-of-the-art applications in language and image generation.
Students engage with the societal dimensions of AI: fairness, transparency, accountability, privacy, and policy implications. They analyze real-world AI systems, examine case studies, and develop informed positions on AI governance.
The capstone is an independent research project where students identify a real-world AI problem, design and implement a solution, and present findings. Students work at a near-professional level, engaging with primary literature, implementing novel approaches, and contributing to the broader understanding of AI.
Grade Level
11-12
Total Units
4