Introduction to Statistical Machine Learning by Masashi Sugiyama - 经管之家

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Introduction to Statistical Machine Learning by Masashi Sugiyama

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收藏 2016-04-10

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science / engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials.

Introduction to Statistical Machine Learning Provides A General Introduction to Machine Learning that Covers A Wide the Range of Topics concisely and Will Help you Bridge at The GAP the BETWEEN Theory and Practice. Part the I Discusses at The Fundamental Concepts of statistics and probability that are Used in Describing Machine Learning algorithms . Part II and Part III explain the two major approaches of machine learning techniques;. generative methods and discriminative methods While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice The accompanying. MATLAB / Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.

Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus.
Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning.
Includes MATLAB / Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks
Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

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Introduction to Statistical Machine Learning

Table of Contents.pdf

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Introduction to Statistical Machine Learning

References, Pages 485-490.pdf

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Introduction to Statistical Machine Learning

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Introduction to Statistical Machine Learning

Part 5 FURTHER TOPICS, Pages 341-342.pdf

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Introduction to Statistical Machine Learning

Part 4 DISCRIMINATIVE APPROACH TO STATISTICAL MACHINE LEARNING, Pages 235-236.pdf

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Introduction to Statistical Machine Learning

Part 3 GENERATIVE APPROACH TO STATISTICAL PATTERN RECOGNITION, Pages 111-112.pdf

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Introduction to Statistical Machine Learning

Part 2 STATISTICS AND PROBABILITY, Pages 9-10.pdf

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Introduction to Statistical Machine Learning

Part 1 INTRODUCTION, Pages 1-2.pdf

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Introduction to Statistical Machine Learning

Half Title Page.pdf

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Introduction to Statistical Machine Learning

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Introduction to Statistical Machine Learning

Chapter 39 - Change Detection, Pages 469-484.pdf

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Introduction to Statistical Machine Learning

Chapter 37 - Clustering, Pages 447-456.pdf

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Introduction to Statistical Machine Learning

Chapter 35 - Linear Dimensionality Reduction, Pages 405-428.pdf

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Introduction to Statistical Machine Learning

Chapter 27 - Support Vector Classification, Pages 303-320.pdf

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Introduction to Statistical Machine Learning

Chapter 18 - Analytic Approximation of Marginal Likelihood, Pages 197-204.pdf

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Introduction to Statistical Machine Learning

Chapter 17 - Bayesian Inference, Pages 185-196.pdf

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Introduction to Statistical Machine Learning

Chapter 16 - Nonparametric Estimation, Pages 169-184.pdf

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Introduction to Statistical Machine Learning

Chapter 15 - Maximum Likelihood Estimation for Gaussian Mixture Model, Pages 157-168.pdf

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Introduction to Statistical Machine Learning

Chapter 14 - Model Selection for Maximum Likelihood Estimation, Pages 147-156.pdf

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Introduction to Statistical Machine Learning

Chapter 13 - Properties of Maximum Likelihood Estimation, Pages 139-146.pdf

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Introduction to Statistical Machine Learning

Chapter 12 - Maximum Likelihood Estimation, Pages 123-138.pdf

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Introduction to Statistical Machine Learning

Chapter 11 - Pattern Recognition via Generative Model Estimation, Pages 113-122.pdf

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Introduction to Statistical Machine Learning

Chapter 10 - Hypothesis Testing, Pages 99-110.pdf

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Introduction to Statistical Machine Learning

Chapter 9 - Statistical Estimation, Pages 91-98.pdf

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Introduction to Statistical Machine Learning

Chapter 8 - Probability Inequalities, Pages 81-90.pdf

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Introduction to Statistical Machine Learning

Chapter 7 - Sum of Independent Random Variables, Pages 73-80.pdf

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Introduction to Statistical Machine Learning

Chapter 6 - Examples of Multidimensional Probability Distributions, Pages 61-72.pdf

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Introduction to Statistical Machine Learning

Chapter 5 - Multidimensional Probability Distributions, Pages 51-60.pdf

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Introduction to Statistical Machine Learning

Chapter 4 - Examples of Continuous Probability Distributions, Pages 37-50.pdf

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Introduction to Statistical Machine Learning

Chapter 3 - Examples of Discrete Probability Distributions, Pages 25-36.pdf

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Introduction to Statistical Machine Learning

Chapter 2 - Random Variables and Probability Distributions, Pages 11-24.pdf

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Introduction to Statistical Machine Learning

Chapter 1 - Statistical Machine Learning, Pages 3-8.pdf

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Introduction to Statistical Machine Learning

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Introduction to Statistical Machine Learning

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2016-4-10 20:32:54

太零散了，打包一下吧，方便网友一次性购买一次性下载吧

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2016-4-10 21:52:05

tigerwolf 发表于 2016-4-10 20:32
太零散了，打包一下吧，方便网友一次性购买一次性下载吧

就是，打包吧。

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2016-4-10 22:28:53

严重缺章： 19-26、 28-34、36、38 章节没有。

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2016-4-10 22:45:41

only bought these chapters, money finish.

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2016-4-10 22:46:57

expensive fragmented chapters on sciencedirect. bought from there.

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