Introduction to Statistical Machine Learning （2ND）

Sugiyama, Masashi/ Ishida, Takashi

Morgan Kaufmann Publishers In（2026/09発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 650 p.
• 言語 ENG
• 商品コード 9780443300325

Full Description

Machine learning allows computers to learn and discern patterns without being programmed. When Statistical techniques and machine learning are combined together, they are a powerful tool for analyzing 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, Second Edition provides a general introduction to machine learning that covers a wide range of topics concisely and will help readers bridge the gap between theory and practice. Parts 1 and 2 discuss the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part 3 and Part 4 explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Parts 5 and 6 provide an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice, including creating full-fledged algorithms in a range of real-world applications drawn from research areas such as image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials. The algorithms developed in the book include Python program code to provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. The Second Edition also includes an all-new Part 6 on on Deep Learning, including chapters on Feedforward Neural Networks, Neural Networks with Image Data, Neural Networks with Sequential Data, learning from limited data, Representation Learning, Deep Generative Modeling, and Multimodal Learning.

Contents

Part 1. Introduction
1. Statistical Machine Learning

Part 2. Statistics and Probability
2. Random Variables and Probability Distributions
3. Examples of Discrete Probability Distributions
4. Examples of Continuous Probability Distributions
5. Multidimensional Probability Distributions
6. Examples of Multidimensional Probability Distributions
7. Sum of Independent Random Variables
8. Probability Inequalities
9. Statistical Estimation
10. Hypothesis Testing

Part 3. Generative Approach to Statistical Pattern Recognition
11. Pattern Recognition via Generative Model Estimation
12. Maximum Likelihood Estimation
13. Properties of Maximum Likelihood Estimation
14. Model Selection for Maximum Likelihood Estimation
15. Maximum Likelihood Estimation for Gaussian Mixture Models
16. Nonparametric Estimation
17. Bayesian Inference
18. Analytic Approximation of Marginal Likelihood
19. Numerical Approximation of Predictive Distribution
20. Bayesian Mixture Models

Part 4. Discriminative Approach to Statistical Machine Learning
21. Learning Models
22. Least Squares Regression
23. Constrained Least Squares Regression
24. Sparse Regression
25. Robust Regression
26. Least Squares Classification
27. Support Vector Classification
28. Probabilistic Classification
29. Structured Classification

Part 5. Further Topics
30. Ensemble Learning
31. Online Learning
32. Confidence of Prediction
33. Weakly Supervised Learning
34. Transfer Learning
35. Multitask Learning
36. Linear Dimensionality Reduction
37. Nonlinear Dimensionality Reduction
38. Clustering
39. Outlier Detection
40. Change Detection

Part 6. Deep Learning
41. Feedforward Neural Networks
42. Neural Networks with Image Data
43. Neural Networks with Sequential Data
44. Learning from Limited Data
45. Representation Learning
46. Deep Generative Modelling
47. Multimodal Learning