| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000045819948 | |
| 005 | 20250722134110 | |
| 008 | 150114s2015 flua b 001 0 eng d | |
| 020 | ▼a 9781466583283 ▼q (hardcover : alk. paper) | |
| 020 | ▼a 1466583282 ▼q (hardcover : alk. paper) | |
| 035 | ▼a (KERIS)REF000017630249 | |
| 040 | ▼a MBB ▼b eng ▼c MBB ▼d MBB ▼d MEAUC ▼d YDXCP ▼d GZN ▼d OCLCQ ▼d 211009 | |
| 050 | 4 | ▼a Q325.5 ▼b .M368 2015 |
| 082 | 0 4 | ▼a 006.3/1 ▼2 23 |
| 084 | ▼a 006.31 ▼2 DDCK | |
| 090 | ▼a 006.31 ▼b M372m2 | |
| 100 | 1 | ▼a Marsland, Stephen, ▼d 1977- ▼0 AUTH(211009)171558. |
| 245 | 1 0 | ▼a Machine learning : ▼b an algorithmic perspective / ▼c Stephen Marsland. |
| 250 | ▼a 2nd ed. | |
| 260 | ▼a Boca Raton : ▼b CRC Press, ▼c c2015. | |
| 300 | ▼a xx, 437 p. : ▼b ill. ; ▼c 27 cm. | |
| 490 | 1 | ▼a Chapman & Hall/CRC machine learning & pattern recognition series |
| 500 | ▼a "A Chapman & Hall book." | |
| 504 | ▼a Includes bibliographical references and index. | |
| 505 | 0 0 | ▼g Introduction -- ▼t Preliminaries -- ▼t Neurons, neural networks, and linear discriminants -- ▼t The multi-layer perceptron -- ▼t Radial basis functions and splines -- ▼t Dimensionality reduction -- ▼t Probabilistic learning -- ▼t Support vector machines -- ▼t Optimisation and search -- ▼t Evolutionary learning -- ▼t Reinforcement learning -- ▼t Learning with trees -- ▼t Decision by committee: ensemble learning -- ▼t Unsupervised learning -- ▼t Markov chain Monte Carlo (MCMC) methods -- ▼t Graphical models -- ▼t Symmetric weights and deep belief networks -- ▼t Gaussian processes -- ▼t Python. |
| 650 | 0 | ▼a Machine learning. |
| 650 | 0 | ▼a Algorithms. |
| 740 | 0 2 | ▼a Multi-layer perceptron. |
| 830 | 0 | ▼a Chapman & Hall/CRC machine learning & pattern recognition series. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 M372m2 | 등록번호 121231802 (23회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
A Proven, Hands-On Approach for Students without a Strong Statistical Foundation
Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.
Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.
New to the Second Edition
- Two new chapters on deep belief networks and Gaussian processes
- Reorganization of the chapters to make a more natural flow of content
- Revision of the support vector machine material, including a simple implementation for experiments
- New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
- Additional discussions of the Kalman and particle filters
- Improved code, including better use of naming conventions in Python
Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.
This bestseller helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. Along with improved Python code, this second edition includes two new chapters on deep belief networks and Gaussian processes. It incorporates new material on the support vector machine, random forests, the perceptron convergence theorem, filters, and more. All of the code is available on the author’s website.
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목차
Introduction. Linear Discriminants. The Multi-Layer Perceptron. Radial Basis Functions and Splines. Support Vector Machines. Learning with Trees. Decision by Committee: Ensemble Learning. Probability and Learning. Unsupervised Learning. Dimensionality Reduction. Optimization and Search. Evolutionary Learning. Reinforcement Learning. Markov Chain Monte Carlo (MCMC) Methods. Graphical Models. Python.
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