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大小: 6.65MB文件类型: .pdf金币: 1下载: 0 次发布日期: 2023-08-12
- 语言: Python
- 标签: Machine Learning Algorithmic
资源简介
Title: Machine Learning: An Algorithmic Perspective, 2nd Edition
Author: Stephen Marsland
Length: 457 pages
Edition: 2
Language: English
Publisher: Chapman and Hall/CRC
Publication Date: 2014-10-08
ISBN-10: 1466583282
ISBN-13: 9781466583283
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.
Table of Contents
Chapter 1: Introduction
Chapter 2: Preliminaries
Chapter 3: Neurons, Neural Networks,and Linear Discriminants
Chapter 4: The Multi-layer Perceptron
Chapter 5: R
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