Quantization and Signal Processing for AI

This course introduces fundamental concepts in signal processing and quantization and explores their role in modern machine learning and AI. Topics include signal representations, Fourier and time-frequency analysis, wavelets, statistical learning, matrix decompositions, quantization, neural networks, and deep learning. Building on classical autoregression and adaptive filtering, the course develops neural sequence models, attention, transformers, and state-space models. Applications include signal classification, prediction, and generation, as well as model compression, mixed-precision computation, and efficient inference for large language models and other generative AI systems.