English | 2026 | ISBN: 0443292388 | 1220 Pages | PDF
Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines.
– Provides a number of case studies and applications on a variety of topics, such as target localization, channel equalization, image denoising, audio characterization, text authorship identification, visual tracking, change point detection, hyperspectral image unmixing, fMRI data analysis, machine translation, and text-to-image generation
– Most chapters include a number of computer exercises in both MatLab and Python, and the chapters dedicated to deep learning include exercises in PyTorch New to this edition
– The new material includes an extended coverage of attention transformers, large language models, self-supervised learning and diffusion models
