This course introduces key signal processing and quantization concepts for modern machine learning and AI. Students learn techniques for capturing, processing, and classifying signals, tracing the roots of quantization in signal processing and its role in generative AI. Topics include signal models, vector spaces, Fourier and time-frequency analysis, Z-transforms, filters, wavelets, autoregression, image and video processing, matrix decompositions, compressed sensing, deep learning, and mixed-precision quantization, with applications ranging from adaptive filters to large language models and other generative AI systems.
3 units · Letter or Credit/No Credit
This course introduces key signal processing and quantization concepts for modern machine learning and AI. Students learn techniques for capturing, processing, and classifying signals, tracing the roots of quantization in signal processing and its role in generative AI. Topics include signal models, vector spaces, Fourier and time-frequency analysis, Z-transforms, filters, wavelets, autoregression, image and video processing, matrix decompositions, compressed sensing, deep learning, and mixed-precision quantization, with applications ranging from adaptive filters to large language models and other generative AI systems.
Offered in Winter 2027 at Stanford University.