Introduction to Statistical Machine Learning
- 2nd Edition - June 1, 2027
- Latest edition
- Authors: Masashi Sugiyama, Takashi Ishida
- Language: English
Introduction to Statistical Machine Learning, Second Edition provides a general introduction to the fundamental concepts of statistics and probability that are used in descri… Read more
Description
Description
Introduction to Statistical Machine Learning, Second Edition provides a general introduction to the fundamental concepts of statistics and probability that are used in describing machine learning algorithms, covering the two major approaches of machine learning techniques, generative methods and discriminative methods. The algorithms developed in the book include Python program code to provide readers with the necessary, practical skills needed to accomplish a wide range of data analysis tasks. The new edition also includes an all-new section on Deep Learning.
Key features
Key features
- Provides the necessary background material to understand machine learning, including statistics, probability, linear algebra, and calculus
- Presents complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning
- Includes Python program code so that readers can test the algorithms numerically and acquire both mathematical and practical skills
Readership
Readership
Computer Science researchers, artificial intelligence researchers, and researchers and practitioners working in the fields of data science, machine learning, and optimization. The primary audience also includes data analysts, software engineers, as well as researchers and professionals across many fields where Machine Learning is being used
Table of contents
Table of 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
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
Review quotes
Review quotes
Review of the previous edition:
"The probabilistic and statistical background is well presented, providing the reader with a complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning."—Zentralblatt MATH
"The probabilistic and statistical background is well presented, providing the reader with a complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning."—Zentralblatt MATH
Product details
Product details
- Edition: 2
- Latest edition
- Published: June 1, 2027
- Language: English
About the authors
About the authors
Masashi Sugiyama
Author
Takashi Ishida is a Research Scientist at RIKEN AIP and an Associate Professor at The University of Tokyo. He received his PhD from the University of Tokyo, advised by Prof. Masashi Sugiyama. Prior to that, he received the MSc from the University of Tokyo and the Bachelor of Economics from Keio University. His research interests include statistical machine learning and its applications, with a focus on model and agent evaluation, alignment, and safety.
Affiliations and expertise
Research Scientist, RIKEN Center for Advanced Intelligence Project, Japan Associate Professor, The University of Tokyo, JapanTakashi Ishida
Author
Takashi Ishida is a Research Scientist at RIKEN AIP and an Associate Professor at The University of Tokyo. He received his PhD from the University of Tokyo, advised by Prof. Masashi Sugiyama. Prior to that, he received the MSc from the University of Tokyo and the Bachelor of Economics from Keio University. His research interests include statistical machine learning and its applications, with a focus on model and agent evaluation, alignment, and safety.
Affiliations and expertise
Research Scientist, RIKEN Center for Advanced Intelligence Project, Japan Associate Professor, The University of Tokyo, Japan