Probabilistic Machine Learning Advanced Topics

□ I. THÔNG TIN SẢN PHẨM
□ Mã sản phẩm : STT1728ab
□ Nhà xuất bản : MIT Press
□ Tác giả : Kevin Patrick Murphy
□ Ngôn ngữ : Tiếng Anh
□ ISBN : 9780262048439
□ Số trang : 1360 trang
□ Hình thức : Bìa Mềm, RUỘT IN ĐEN TRẮNG, BÌA IN MẪU LASER GIẤY C300 CÓ CÁN
□ Loại : Sách gia công đóng gáy keo chắc chắn chất lượng cao
□ Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và highlight thoải mái.
□ Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.

□ II. MÔ TẢ SẢN PHẨM
□ 1.Mô tả sản phẩm đầy đủ
Probabilistic Machine Learning: Advanced Topics is a comprehensive and high-level textbook designed for researchers and graduate students seeking an in-depth understanding of the most advanced concepts in machine learning. Serving as a sequel and advanced counterpart to Murphy’s introductory volume, this book provides a rigorous exploration of cutting-edge fields, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. One of the book’s primary strengths is its ability to place modern deep learning within a broader statistical context, effectively unifying neural network approaches with traditional probabilistic modeling and inference techniques. The text features contributions from leading scientists and domain experts from prestigious institutions and companies such as Google DeepMind, Amazon, MIT, and NYU, ensuring that the content reflects the current state of the art in both industry and academia. The book is structured to guide readers through complex mathematical and computational tools, moving well beyond basic concepts to discuss sophisticated methods for high-dimensional data. Key topics include the generation of complex outputs like images, text, and graphs, and the discovery of latent insights through variable models. It also delves into the challenges of training and testing under varying distributions, and the application of probabilistic models to causal reasoning and decision-making under uncertainty. To bridge the gap between theory and practice, the book is accompanied by extensive online Python code, allowing readers to experiment with the algorithms and models discussed. Praised by luminaries like Geoffrey Hinton and Yoshua Bengio, this volume is considered a landmark achievement that systematizes decades of progress in the field. Whether used as a classroom textbook or a professional reference, it provides an essential foundation for anyone looking to master the intricacies of modern machine learning and participate in the next wave of artificial intelligence research. Its clear notation and intuitive explanations make even the most daunting topics accessible to a dedicated audience of scholars and practitioners.

□ 2. Tác giả
Kevin Patrick Murphy is a Principal Scientist at Google DeepMind in Mountain View, California, where he leads research on artificial intelligence, machine learning, and Bayesian modeling. Born in Ireland and raised in England, Murphy earned his BA from the University of Cambridge, followed by an MEng from the University of Pennsylvania and a PhD in Computer Science from UC Berkeley. After completing a postdoctoral fellowship at the MIT AI Lab, he served as an Associate Professor of Computer Science and Statistics at the University of British Columbia from 2004 to 2012. He joined Google full-time in 2012 and has since become a leading figure in the ML community. His seminal 2012 textbook, Machine Learning: A Probabilistic Perspective, was awarded the prestigious DeGroot Prize. Murphy has published over 140 refereed papers and has served as Editor-in-Chief for the Journal of Machine Learning Research.