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Introduction to Statistical Machine Learning
- 2nd Edition - August 1, 2025
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 3 2 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 3 3 - 2
Machine learning allows computers to learn and discern patterns without being programmed. When Statistical techniques and machine learning are combined together, they are a… Read more
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Request a sales quoteMachine 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.
• 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 Python program code 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.
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 Python program code 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.
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.
Part 1. Introduction
Chapter 1. Statistical Machine Learning
Part 2. Statistics and probability
Chapter 2. Random Variables and Probability Distributions
Chapter 3. Examples of Discrete Probability Distributions
Chapter 4. Examples of Continuous Probability Distributions
Chapter 5. Multidimensional Probability Distributions
Chapter 6. Examples of Multidimensional Probability Distributions
Chapter 7. Sum of Independent Random Variables
Chapter 8. Probability Inequalities Chapter 9. Statistical Estimation
Chapter 10. Hypothesis Testing
Part 3. Generative approach to statistical pattern recognition
Chapter 11. Pattern Recognition via Generative Model Estimation
Chapter 12. Maximum Likelihood Estimation
Chapter 13. Properties of Maximum Likelihood Estimation
Chapter 14. Model Selection for Maximum Likelihood Estimation
Chapter 15. Maximum Likelihood Estimation for Gaussian Mixture Model
Chapter 16. Nonparametric Estimation
Chapter 17. Bayesian Inference
Chapter 18. Analytic Approximation of Marginal Likelihood
Chapter 19. Numerical Approximation of Predictive Distribution
Chapter 20. Bayesian Mixture Models
Part 4. Discriminative approach to statistical machine learning
Chapter 21. Learning Models
Chapter 22. Least Squares Regression
Chapter 23. Constrained LS Regression
Chapter 24. Sparse Regression
Chapter 25. Robust Regression
Chapter 26. Least Squares Classification
Chapter 27. Support Vector Classification
Chapter 28. Probabilistic Classification
Chapter 29. Structured Classification
Part 5. Further topics
Chapter 30. Ensemble Learning
Chapter 31. Online Learning
Chapter 32. Confidence of Prediction
Chapter 33. Weakly supervised Learning
Chapter 34. Transfer Learning
Chapter 35. Multitask Learning
Chapter 36. Linear Dimensionality Reduction
Chapter 37. Nonlinear Dimensionality Reduction
Chapter 38. Clustering
Chapter 39. Outlier detection
Chapter 40. Change detection
Part 6. Deep learning
Chapter 41. Feedforward neural networks
Chapter 42. Neural networks with image data
Chapter 43. Neural networks with sequential data
Chapter 44. Learning from limited data
Chapter 45. Representation learning
Chapter 46. Deep generative modelling
Chapter 47. Multimodal learning
Chapter 1. Statistical Machine Learning
Part 2. Statistics and probability
Chapter 2. Random Variables and Probability Distributions
Chapter 3. Examples of Discrete Probability Distributions
Chapter 4. Examples of Continuous Probability Distributions
Chapter 5. Multidimensional Probability Distributions
Chapter 6. Examples of Multidimensional Probability Distributions
Chapter 7. Sum of Independent Random Variables
Chapter 8. Probability Inequalities Chapter 9. Statistical Estimation
Chapter 10. Hypothesis Testing
Part 3. Generative approach to statistical pattern recognition
Chapter 11. Pattern Recognition via Generative Model Estimation
Chapter 12. Maximum Likelihood Estimation
Chapter 13. Properties of Maximum Likelihood Estimation
Chapter 14. Model Selection for Maximum Likelihood Estimation
Chapter 15. Maximum Likelihood Estimation for Gaussian Mixture Model
Chapter 16. Nonparametric Estimation
Chapter 17. Bayesian Inference
Chapter 18. Analytic Approximation of Marginal Likelihood
Chapter 19. Numerical Approximation of Predictive Distribution
Chapter 20. Bayesian Mixture Models
Part 4. Discriminative approach to statistical machine learning
Chapter 21. Learning Models
Chapter 22. Least Squares Regression
Chapter 23. Constrained LS Regression
Chapter 24. Sparse Regression
Chapter 25. Robust Regression
Chapter 26. Least Squares Classification
Chapter 27. Support Vector Classification
Chapter 28. Probabilistic Classification
Chapter 29. Structured Classification
Part 5. Further topics
Chapter 30. Ensemble Learning
Chapter 31. Online Learning
Chapter 32. Confidence of Prediction
Chapter 33. Weakly supervised Learning
Chapter 34. Transfer Learning
Chapter 35. Multitask Learning
Chapter 36. Linear Dimensionality Reduction
Chapter 37. Nonlinear Dimensionality Reduction
Chapter 38. Clustering
Chapter 39. Outlier detection
Chapter 40. Change detection
Part 6. Deep learning
Chapter 41. Feedforward neural networks
Chapter 42. Neural networks with image data
Chapter 43. Neural networks with sequential data
Chapter 44. Learning from limited data
Chapter 45. Representation learning
Chapter 46. Deep generative modelling
Chapter 47. Multimodal learning
- No. of pages: 650
- Language: English
- Edition: 2
- Published: August 1, 2025
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780443300325
- eBook ISBN: 9780443300332