Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / 2nd ed
| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000046076309 | |
| 005 | 20230612140228 | |
| 008 | 210405s2020 maua b 001 0 eng | |
| 010 | ▼a 2020002998 | |
| 020 | ▼a 9780262044691 (hardcover) | |
| 035 | ▼a (KERIS)REF000019229274 | |
| 040 | ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009 | |
| 042 | ▼a pcc | |
| 050 | 0 0 | ▼a Q325.5 ▼b .K455 2020 |
| 082 | 0 4 | ▼a 006.31 ▼2 23 |
| 082 | 0 0 | ▼a 519.2/870285631 ▼2 23 |
| 084 | ▼a 006.31 ▼2 DDCK | |
| 090 | ▼a 006.31 ▼b K29f2 | |
| 100 | 1 | ▼a Kelleher, John D., ▼d 1974- ▼0 AUTH(211009)90227. |
| 245 | 1 0 | ▼a Fundamentals of machine learning for predictive data analytics : ▼b algorithms, worked examples, and case studies / ▼c John D. Kelleher, Brian MacNamee and Aoife D'Arcy. |
| 250 | ▼a 2nd ed. | |
| 260 | ▼a Cambridge, Massachusetts : ▼b MIT Press, ▼c c2020. | |
| 264 | 1 | ▼a Cambridge, Massachusetts : ▼b The MIT Press, ▼c 2020. |
| 300 | ▼a liv, 798 p. : ▼b ill. ; ▼c 25 cm. | |
| 336 | ▼a text ▼b txt ▼2 rdacontent | |
| 337 | ▼a unmediated ▼b n ▼2 rdamedia | |
| 338 | ▼a volume ▼b nc ▼2 rdacarrier | |
| 504 | ▼a Includes bibliographical references and index. | |
| 520 | ▼a "A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications"-- ▼c Provided by publisher. | |
| 650 | 0 | ▼a Machine learning. |
| 650 | 0 | ▼a Data mining. |
| 650 | 0 | ▼a Prediction theory. |
| 700 | 1 | ▼a Mac Namee, Brian, ▼e author. |
| 700 | 1 | ▼a D'Arcy, Aoife, ▼d 1978-, ▼e author. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 006.31 K29f2 | 등록번호 111846746 (1회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.
About the Author
John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at Technological University Dublin. He is the coauthor of Data Science and the author of Deep Learning, both in the MIT Press Essential Knowledge series.Brian Mac Namee is Associate Professor at the School of Computer Science at University College Dublin
Aoife D'Arcy is CEO of Krisolis, a data analytics company based in Dublin.
정보제공 :
저자소개
존 켈러허(지은이)
더블린공과대학교 컴퓨터과학부 교수이자 부속기관인 정보통신 및 엔터테인먼트 연구소 소장. 인공지능, 기계학습 분야에서 세계적으로 인정받는 전문가다. 더블린시립대학교, 유럽미디어연구소, 독일인공지능연구센터 등 여러 대학과 연구소에서 일했다. 지은 책으로 《딥러닝》 《데이터 예측을 위한 머신 러닝》(공저)이 있다.
브라이언 맥 네미(지은이)
아일랜드 더블린에 살고 있으며, 더블린 대학의 강사이자, Analytics Store의 이사이다. 데이터 분석, 머신 러닝, 데이터 시각화, 인공 지능에 관해 고민하고 글 쓰는 데 많은 시간을 보낸다.
이퍼 다시(지은이)
2009년 컨설팅 및 교육 회사 Analytics Store를 설립했다. 이 회사는 고급 데이터 마이닝 및 분석 기술들을 이용해 고객이 데이터에서 실행 가능한 통찰을 끌어낼 수 있도록 도와 준다. Analytics Store의 이사이자 수석 컨설턴트로 여러 회사와 함께 사기 검출, 신용 위험, 고객 통찰 등에 대한 해법을 개발해왔다. 또한 고객과 협력해 데이터 마이닝 및 분석에 대한 맞춤식 교육 과정을 개발하고 제공한다.