Introduction to Statistical Machine Learning. Edition No. 2

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

Authors

Masashi Sugiyama Professor, The University of Tokyo, Japan. Masashi Sugiyama received the degrees of Bachelor of Engineering, Master of Engineering, and Doctor of Engineering in Computer Science from Tokyo Institute of Technology, Japan in 1997, 1999, and 2001, respectively. In 2001, he was appointed Assistant Professor in the same institute, and he was promoted to Associate Professor in 2003. He moved to the University of Tokyo as Professor in 2014. He received an Alexander von Humboldt Foundation Research Fellowship and researched at Fraunhofer Institute, Berlin, Germany, from 2003 to 2004. In 2006, he received a European Commission Program Erasmus Mundus Scholarship and researched at the University of Edinburgh, Edinburgh, UK. He received the Faculty Award from IBM in 2007 for his contribution to machine learning under non-stationarity, the Nagao Special Researcher Award from the Information Processing Society of Japan in 2011 and the Young Scientists' Prize from the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology Japan for his contribution to the density-ratio paradigm of machine learning. His research interests include theories and algorithms of machine learning and data mining, and a wide range of applications such as signal processing, image processing, and robot control. Takashi Ishida Lecturer, Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Japan. Dr. Takashi Ishida is a Lecturer at Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo. He is also affiliated with Department of Computer Science, Graduate School of Information Science and Technology and Department of Information Science, Faculty of Science. Dr. Ishida received his PhD from the University of Tokyo in 2021, advised by Prof. Masashi Sugiyama. Prior to that, he received the MSc from the University of Tokyo in September 2017 and the Bachelor of Economics from Keio University in March 2013.