Introduction to Machine Learning

The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing.

Introduction to Machine Learning

E. Alpaydin

Massachusetts Institute of Technology

2020

Abstract

A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks.

The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.

Contents:

  1. Supervised learning
  2. Bayesian decision theory
  3. Parametric methods
  4. Multivariate methods
  5. Dimensionality reduction
  6. Clusering; nonparametric methods
  7. Decision trees
  8. Linear discrimination
  9. Multilayer perceptrons
  10. Ddeep learning
  11. Local models
  12. Kernel machines
  13. Graphical models
  14. Hidden markov models
  15. Bayesian estimation
  16. Combining multiple learners
  17. Reinforcement learning
  18. Design and analysis of machine learning experiments.

Citation

E. Alpaydin, Introduction to Machine Learning, Massachusetts Institute of Technology, 2020

Collection

Bộ sưu tập số Lĩnh vực Cơ khí, Chế tạo máy

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