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Machine Learning: An Algorithmic Perspective, 2/E
출판사 : CRC
저 자 : Marsland
ISBN : 9781466583283
발행일 : 2014-07
도서종류 : 외국도서
발행언어 : 영어
페이지수 : 457
판매가격 : 52,000원
판매여부 : 재고확인요망
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   Machine Learning: An Algorithmic Perspective, 2/E 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.




   도서 상세설명   

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.

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