Introduction to Machine Learning, fourth edition

Предња корица
MIT Press, 24. 3. 2020. - 712 страница
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.

 

Садржај

1 Introduction
1
2 Supervised Learning
23
3 Bayesian Decision Theory
51
4 Parametric Methods
67
5 Multivariate Methods
95
6 Dimensionality Reduction
117
7 Clustering
165
8 Nonparametric Methods
189
14 Kernel Machines
395
15 Graphical Models
433
16 Hidden Markov Models
463
17 Bayesian Estimation
491
18 Combining Multiple Learners
533
19 Reinforcement Learning
563
20 Design and Analysis of Machine Learning Experiments
597
A Probability
643

9 Decision Trees
217
10 Linear Discrimination
243
11 Multilayer Perceptrons
271
12 Deep Learning
313
13 Local Models
361
B Linear Algebra
655
C Optimization
665
Index
673
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О аутору (2020)

Ethem Alpaydin is Professor in the Department of Computer Engineering at Özyegin University and Member of The Science Academy, Istanbul. He is the author of Machine Learning: The New AI, a volume in the MIT Press Essential Knowledge series.s).

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