Machine learning A Probabilistic Perspective.pdf

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. 优点：新，全！ 由于成书时间较晚，所以涵盖了更多最近几年的hot topic，比如Dirichlet Process 。 更重要的，是全，基本上ML领域的专有名词，你都可以在书后的index找到。说道这里，不得不佩服本书的作者Kevin Murphy，剑桥的本科，UCB的博士，MIT的博后，得到过多位大牛的真传 。 还有一个非常重要的，就是这本书配备了详尽的matlab code，你几乎可以尝试书中的每一个例子。 单从以上这几点，绝对应该把他排在所有ML教材的首位！