PACKT (406)
Text Book 교재용원서 (673)
컴퓨터공학 (819)
컴퓨터 일반도서 (555)
전기,전자공학 (710)
기계공학 (197)
재료공학 (34)
에너지공학 (65)
의용공학 (39)
생명과학 (229)
물리학 (426)
지구과학 (74)
천문학 (39)
수학 (103)
통계학 (45)
경영학 (40)
산업공학 (12)
사회복지학 (5)
심리학 (247)
교육학 (1)
화학 (5)
기타 (64)
특가할인도서 (택배비별도) (87)

> > 컴퓨터공학 > 인공지능

이미지를 클릭하시면 큰 이미지를 보실 수 있습니다.
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
주문수량 : [+]수량을 1개 늘입니다 [-]수량을 1개 줄입니다

My Wish List 에 저장하기
   Machine Learning: An Algorithmic Perspective, 2/E 목차

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.


   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.

  교육용 보조자료   
작성된 교육용 보조자료가 없습니다.