Machine Learning pdf epub mobi txt 電子書 下載2025
- 機器學習
- MachineLearning
- 數據挖掘
- 計算機
- 計算機科學
- 概率
- 統計
- 人工智能
- Machine Learning
- 人工智能
- 算法
- 數據科學
- 深度學習
- 編程
- 模型
- 訓練
- 預測
- 分類
具體描述
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.
著者簡介
Kevin P. Murphy is Associate Professor in the Department of Computer Science and in the Department of Statistics at the University of British Columbia.
圖書目錄
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
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讀後感
判别模型几乎没怎么讲。。 后面各种生成模型，贝叶斯网、随机场、MCMC、HMM。 ==========================================================================================================================================================
評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...
評分 評分判别模型几乎没怎么讲。。 后面各种生成模型，贝叶斯网、随机场、MCMC、HMM。 ==========================================================================================================================================================
評分-----------------------------读完第三章更新------------------------------ 啪啪啪啪啪啪啪啪啪啪啪，先自扇十个大耳光。 这本书还是不错的，很深，我写了个第三章的笔记，欢迎拍砖。http://book.douban.com/annotation/23203104/ 第三章可读性比第二章好得多，但是说实话还...
用戶評價
Chapter 1-3, 07.09.2019; C4 (Gaussian models) 07.12; C5 (Bayesian statistics) 07.19;C6 (Frequentist statistics) 07.20; C7 (Linear regression) 07.29; C8 (Logistic regression) 08.22
评分這本書優點就是很全麵，韆餘頁的大部頭，啥都有。缺點也是很全麵，每一個點都不太細緻，還需要自己去找論文看。
评分內容很全麵，但感覺章節安排的順序可以稍微調整一下。
评分太執著於一個學派也不好。大坑慎入。 Important chapters 4 me: Chaps.3-12, 14, 17, 19 & 25.
评分machine learning教材
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