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71zMmruR+uL._UF1000,1000_QL80_.jpg,Pattern Recognition and Machine Learning (Information,Pattern Recognition and Machine Learning (Information,Pattern Recognition & Machine Learning (06) by [Hardcover,Deep Learning vs. Machine Learning vs. Pattern Recognition,Pattern Recognition and Machine Learning (Information Science and Statistics) 2006年巻頭に8ページの黒ペンの書き込みありOPP袋に入れて、宅急便コンパクト専用梱包材に入れて、佐川急便/日本郵便での発送を予定しています。洋書 jock sturges。\rコメントなしでの即購入歓迎。上野動物園パンダシャンシャン写真集（日本未発売）「被宠溺的表情　香香在东京」国宝。Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.