Bayesian reasoning and machine learning
Bayesian reasoning and machine learning
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DOI：
10.1017/cbo9780511804779.017
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发表时间：
2012-03
期刊：
影响因子：
--
通讯作者：
D. Barber
中科院分区：
文献类型：
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作者：
D. Barber
机器学习方法利用有限的资源快速从海量数据集中提取价值。它们是广泛工业应用中的既定工具，包括搜索引擎、DNA 测序、股票市场分析和机器人运动，并且它们的使用正在迅速蔓延。懂得这些方法的人可以选择有价值的工作。这本实践性的文本为具有适度数学背景的计算机科学学生提供了这些机会。它是为线性代数和微积分背景有限的最后一年本科生和硕士生设计的。它全面而连贯，在图形模型的框架内开发了从基本推理到高级技术的所有内容。学生学习的不仅仅是一系列技术，他们还培养分析和解决问题的技能，为现实世界做好准备。每章都包含大量基于计算机和理论的示例和练习。为学生和教师提供的资源（包括 MATLAB 工具箱）可在线获取。
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.