Advanced ML Math

This chapter covers the mathematical foundations behind modern ML architectures that require mathematical tools beyond the classical toolkit.

• Attention and Transformers -- Scaled dot-product attention, multi-head attention, positional encoding, FlashAttention
• Diffusion Models -- Forward noise process, reverse denoising, DDPM loss, score matching
• Graph Neural Networks -- Graph Laplacian, spectral theory, message passing, GCN/GAT
• Optimal Transport -- Wasserstein distance, Kantorovich duality, Sinkhorn algorithm