内 容 提 要本书是对以数据深度需求为中心的科学、研究以及针对计算和统计方法的参考书。本书共五章,每章介绍一到两个Python 数据科学中的重点工具包。首先从IPython 和Jupyter 开始,它们提供了数据科学家需要的计算环境;第2 章讲解能提供ndarray 对象的NumPy,它可以用Python 高效地存储和操作大型数组;第3 章主要涉及提供DataFrame 对象的Pandas,它可以用Python 高效地存储和操作带标签的/ 列式数据;第4 章的主角是Matplotlib,它为Python 提供了许多数据可视化功能;第5 章以Scikit-Learn 为主,这个程序库为最重要的机器学习算法提供了高效整洁的Python 版实现。本书适合有编程背景,并打算将开源Python 工具用作分析、操作、可视化以及学习数据的数据科学研究人员。

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zlib/Engineering/Computer Technology/[美] Jake VanderPlas/[图灵程序设计丛书].Python数据科学手册_22277738.pdf

Pattern Recognition and Machine Learning (Information Science and Statistics)

Bishop, Christopher M.

Christopher M. Bishop

Springer US

Copernicus

Telos

Information science and statistics, 8th print, New York, 2009

Information science and statistics, Cham, Switzerland, 2006

Information science and statistics, New York, 2006

United States, United States of America

January 5, 2008

PT, 2006

producers:
iTextSharpTM 5.5.10 ©2000-2016 iText Group NV (AGPL-version)

Introduction. Example : Polynomial Curve Fitting ; Probability Theory ; Model Selection ; The Curse Of Dimensionality Decision Theory ; Information Theory -- Probability Distributions. Binary Vehicles ; Multinomial Variables ; The Gaussian Distribution ; The Exponential Family ; Nonparametric Methods -- Linear Models For Regression. Linear Basis Function Models ; The Bias-variance Decomposition ; Bayesian Linear Regression ; Bayesian Model Comparison ; The Evidence Approximation ; Limitations Of Fixed Basis Functions -- Linear Models For Classification. Discriminant Functions ; Probabilistic Generative Models ; Probabilistic Discrimitive Models ; The Laplace Approximation ; Bayesian Logistic Regression -- Neural Networks. Feed-forward Network Functions ; Network Training ; Error Backpropagation ; The Hessian Matrix ; Regularization In Neural Networks ; Mixture Density Networks ; Bayesian Neural Networks. Kernel Methods. Dual Representations ; Constructing Kernals ; Radial Basis Function Networks ; Gaussian Processes -- Sparse Kernel Machines. Maximum Margin Classifiers ; Relevance Vector Machines -- Graphical Models. Bayesian Networks ; Conditional Independence ; Markov Random Fields ; Inference In Graphical Models -- Mixture Models And Em. K-means Clustering ; Mixtures Of Gaussians ; An Alternative View Of Em ; The Em Algorithm In General -- Approximate Inference. Variational Inference ; Illustration : Variational Mixture Of Gaussians ; Variational Linear Regression ; Exponential Family Distributions ; Local Variational Methods ; Variational Logistic Regression ; Expectation Propagation -- Sampling Methods. Basic Sampling Algorithms ; Markov Chain Monte Carlo ; Gibbs Sampling ; Slice Sampling ; The Hybrid Monte Carlo Algorithm ; Estimating The Partition Function. Continuous Latent Variables. Principal Component Analysis ; Probabilistic Pca ; Kernel Pca ; Nonlinear Latent Variable Models -- Sequential Data. Markoc Models ; Hidden Markov Models ; Linear Dynamical Systems -- Combining Models. Bayesian Model Averaging ; Committees ; Boosting ; Tree-based Models ; Conditional Mixture Models -- Data Sets -- Probability Distributions -- Properties Of Matrices -- Calculus Of Variations -- Lagrange Multipliers. Christopher M. Bishop. Includes Bibliographical References (p. 711-728) And Index.

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, 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 techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Christopher M. Bishop is Deputy Director of Microsoft Research Cambridge, and holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society of Edinburgh. His previous textbook "Neural Networks for Pattern Recognition" has been widely adopted

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. This new textbook reacts these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as wellas researchers and practitioners, and assumes no previous knowledge of pattern recognition or - chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Erscheinungsdatum: 17.08.2006

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 propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners

2022-08-07