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| 005 | 20241106155429 | |
| 008 | 241105s2020 mauad b 001 0 eng | |
| 010 | ▼a 2019028373 | |
| 020 | ▼a 9780262043793 ▼q (hardcover) | |
| 020 | ▼z 9780262358064 ▼q (ebook) | |
| 035 | ▼a (KERIS)REF000019045577 | |
| 040 | ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009 | |
| 042 | ▼a pcc | |
| 050 | 0 0 | ▼a Q325.5 ▼b .A46 2020 |
| 082 | 0 0 | ▼a 006.3/1 ▼2 23 |
| 084 | ▼a 006.31 ▼2 DDCK | |
| 090 | ▼a 006.31 ▼b A456i4 | |
| 100 | 1 | ▼a Alpaydin, Ethem ▼0 AUTH(211009)147771. |
| 245 | 1 0 | ▼a Introduction to machine learning / ▼c Ethem Alpaydin. |
| 250 | ▼a 4th ed. | |
| 260 | ▼a Cambridge, Massachusetts : ▼b The MIT Press, ▼c 2020. | |
| 264 | 1 | ▼a Cambridge, Massachusetts : ▼b The MIT Press, ▼c [2020] |
| 300 | ▼a xxiv, 682 p. : ▼b ill., charts ; ▼c 24 cm. | |
| 336 | ▼a text ▼b txt ▼2 rdacontent | |
| 337 | ▼a unmediated ▼b n ▼2 rdamedia | |
| 338 | ▼a volume ▼b nc ▼2 rdacarrier | |
| 490 | 1 | ▼a Adaptive computation and machine learning series |
| 504 | ▼a Includes bibliographical references and index. | |
| 520 | ▼a "Since the third edition of this text appeared in 2014, most recent advances in machine learning, both in theory and application, are related to neural networks and deep learning. In this new edition, the author has extended the discussion of multilayer perceptrons. He has also added a new chapter on deep learning including training deep neural networks, regularizing them so they learn better, structuring them to improve learning, e.g., through convolutional layers, and their recurrent extensions with short-term memory necessary for learning sequences. There is a new section on generative adversarial networks that have found an impressive array of applications in recent years. Alpaydin has also extended the chapter on reinforcement learning to discuss the use of deep networks in reinforcement learning. There is a new section on the policy gradient method that has been used frequently in recent years with neural networks, and two additional sections on two examples of deep reinforcement learning, which both made headlines when they were announced in 2015 and 2016 respectively. One is a network that learns to play arcade video games, and the other one learns to play Go. There are also revisions in other chapters reflecting new approaches, such as embedding methods for dimensionality reduction, and multi-label classification. In response to requests from instructors, this new edition contains two new appendices on linear algebra and optimization, to remind the reader of the basics of those topics that find use in machine learning"--Provided by publisher. | |
| 650 | 0 | ▼a Machine learning. |
| 830 | 0 | ▼a Adaptive computation and machine learning series. |
| 945 | ▼a ITMT |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info/지정도서 | 청구기호 006.31 A456i4 | 등록번호 121267746 (4회 대출) | 도서상태 지정도서 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
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.
About the Author
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).정보제공 :
저자소개
에템 알페이딘(지은이)
로잔공과대학에서 박사 학위를 받고 터키 이스탄불의 명문 보아지치대학교 컴퓨터공학과 교수로 재직 중이다. 세계적인 인공지능 전문가로 머신러닝을 전문적으로 연구하고 있다. 머신러닝 교과서로 널리 사용되고 있는 머신러닝 개론(Introduction to Machine Learning)의 저자다.
목차
1 Introduction
2 Supervised Learning
3 Bayesian Decision Theory
4 Parametric Methods
5 Multivariate Methods
6 Dimensionality Reduction
7 Clustering
8 Nonparametric Methods
9 Decision Trees
10 Linear Discrimination
11 Multilayer Perceptrons
12 Deep Learning
13 Local Models
14 Kernel Machines
15 Graphical Models
16 Hidden Markov Models
17 Bayesian Estimation
18 Combining Multiple Learners
19 Reinforcement Learning
20 Design and Analysis of Machine Learning Experiments
A Probability
B Linear Algebra
C Optimization
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