Machine Learning pdf epub mobi txt 电子书 下载 2025
- 机器学习
- MachineLearning
- 数据挖掘
- 计算机
- 计算机科学
- 概率
- 统计
- 人工智能
- Machine Learning
- 人工智能
- 算法
- 数据科学
- 深度学习
- 编程
- 模型
- 训练
- 预测
- 分类
具体描述
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
作者简介
Kevin P. Murphy is Associate Professor in the Department of Computer Science and in the Department of Statistics at the University of British Columbia.
目录信息
Chapter 2: Probability
Chapter 3: Statistics
Chapter 4: Gaussian models
Chapter 5: Generative models for classification
Chapter 6: Discriminative linear models
Chapter 7: Graphical Models
Chapter 8: Decision theory
Chapter 9: Mixture models and the EM algorithm
Chapter 10: Latent Linear models
Chapter 11: Hierarchical Bayes
Chapter 12: Sparce Linear Models
Chapter 13: Kernels
Chapter 14: Gaussian processes
Chapter 15: Adaptive basis function models
Chapter 16: Markov and hidden Markov Models
Chapter 17: State space models
Chapter 18: Conditional random fields
Chapter 19: Exact inference algorithms for graphical models
Chapter 20: Mean field inference algorithms
Chapter 21: Other variational inference algorithms
Chapter 22: Monte Carlo inference algorithms
Chapter 23: MCMC inference algorithms
Chapter 24: Clustering
Chapter 25: Graphical model structure learning
Chapter 26: Two-layer latent variable models
Chapter 27: Deep learning
· · · · · · (收起)
读后感
这是我为本书第四次（我买的是第六次印刷，但是是一样的）印刷写的勘误表：https://github.com/ks838/Murphy-Machine-Learning-A-Probabilistic-Perspective-Errata-and-Notes-4th-printing
评分我们正准备读这本书，Machine Learning A Probabilistic Perspective 读书会请加qq群177217565，也讨论Pattern Recognition And Machine Learning。
评分我们正准备读这本书，Machine Learning A Probabilistic Perspective 读书会请加qq群177217565，也讨论Pattern Recognition And Machine Learning。
评分这本书的作者试图把机器学习进行全景式地展现，根据我有限的机器学习知识，作者把机器学习该有的都涵盖了。 这样做一个非常大的缺陷就是东西太多，讲的不够深入，许多例子都是非常笼统，没有做详细解释，就给了一个图，随便说了几句，对于一个初学者，怎么可能理解的了。 书中...
评分这本书的作者试图把机器学习进行全景式地展现，根据我有限的机器学习知识，作者把机器学习该有的都涵盖了。 这样做一个非常大的缺陷就是东西太多，讲的不够深入，许多例子都是非常笼统，没有做详细解释，就给了一个图，随便说了几句，对于一个初学者，怎么可能理解的了。 书中...
用户评价
应当会像PRML一样称雄Machine Learning榜单至少四五年吧
评分经典教材
评分感觉有点泛泛
评分不够系统，有点乱，小错有点多。瑕不掩瑜，仍是经典。Machine Learning就两本书，PRML和这本。
评分:无
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