Mathematical & Technical Foundations

The training process, data requirements, and safety implications

Vectors, matrices, eigenvalues, and transformations

Survey of different AI architectures and their safety implications

Deep dive into how LLMs work and their unique safety considerations

Derivatives, gradients, and optimization algorithms

Distributions, inference, and Bayesian thinking for AI safety

NumPy, PyTorch, and essential programming skills