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 ageneral introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses 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. Content: Half Title Page,Title Page,Copyright,Table of Contents,Biography,PrefaceEntitled to full textPart 1INTRODUCTION, Pages 1-2 Chapter 1 - Statistical Machine Learning, Pages 3-8 STATISTICS AND PROBABILITY, Pages 9-10 Chapter 2 - Random Variables and Probability Distributions, Pages 11-24 Chapter 3 - Examples of Discrete Probability Distributions, Pages 25-36 Chapter 4 - Examples of Continuous Probability Distributions, Pages 37-50 Chapter 5 - Multidimensional Probability Distributions, Pages 51-60 Chapter 6 - Examples of Multidimensional Probability Distributions, Pages 61-72 Chapter 7 - Sum of Independent Random Variables, Pages 73-80 Chapter 8 - Probability Inequalities, Pages 81-90 Chapter 9 - Statistical Estimation, Pages 91-98 Chapter 10 - Hypothesis Testing, Pages 99-110 GENERATIVE APPROACH TO STATISTICAL PATTERN RECOGNITION, Pages 111-112 Chapter 11 - Pattern Recognition via Generative Model Estimation, Pages 113-122 Chapter 12 - Maximum Likelihood Estimation, Pages 123-138 Chapter 13 - Properties of Maximum Likelihood Estimation, Pages 139-146 Chapter 14 - Model Selection for Maximum Likelihood Estimation, Pages 147-156 Chapter 15 - Maximum Likelihood Estimation for Gaussian Mixture Model, Pages 157-168 Chapter 16 - Nonparametric Estimation, Pages 169-184 Chapter 17 - Bayesian Inference, Pages 185-196 Chapter 18 - Analytic Approximation of Marginal Likelihood, Pages 197-204 Chapter 19 - Numerical Approximation of Predictive Distribution, Pages 205-220 Chapter 20 - Bayesian Mixture Models, Pages 221-234 DISCRIMINATIVE APPROACH TO STATISTICAL MACHINE LEARNING, Pages 235-236 Chapter 21 - Learning Models, Pages 237-244 Chapter 22 - Least Squares Regression, Pages 245-256 Chapter 23 - Constrained LS Regression, Pages 257-266 Chapter 24 - Sparse Regression, Pages 267-278 Chapter 25 - Robust Regression, Pages 279-294 Chapter 26 - Least Squares Classification, Pages 295-302 Chapter 27 - Support Vector Classification, Pages 303-320 Chapter 28 - Probabilistic Classification, Pages 321-328 Chapter 29 - Structured Classification, Pages 329-340 FURTHER TOPICS, Pages 341-342 Chapter 30 - Ensemble Learning, Pages 343-354 Chapter 31 - Online Learning, Pages 355-364 Chapter 32 - Confidence of Prediction, Pages 365-374 Chapter 33 - Semisupervised Learning, Pages 375-390 Chapter 34 - Multitask Learning, Pages 391-404 Chapter 35 - Linear Dimensionality Reduction, Pages 405-428 Chapter 36 - Nonlinear Dimensionality Reduction, Pages 429-446 Chapter 37 - Clustering, Pages 447-456 Chapter 38 - Outlier Detection, Pages 457-468 Chapter 39 - Change Detection, Pages 469-484 References, Pages 485-490 Pages 491-498
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 range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses 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.
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 range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses 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
