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도서 소개

인공지능(제4판): 현대적 접근방식 2권

전 세계 1,500개 이상의 대학에서 교재로 사용하는 인공지능 교과서!

 

도서구매 사이트(가나다순)

 [교보문고]  [도서11번가]  [알라딘]  [예스이십사]  [인터파크]  [쿠팡]

출판사 제이펍
저작권사 Pearson
원서명 Artificial Intelligence: A Modern Approach 4/E(ISBN: 9780134610993)
지은이 스튜어트 러셀, 피터 노빅
옮긴이 류광
출판일 2021년 8월 25일
페이지 628쪽
판   형 46배판변형(188 x 245 x 26.5)
제   본 무선(soft cover)
정   가 38,000원
ISBN 979-11-91600-32-2 (93000)
키워드 인공지능, AI, 로봇공학, 기계학습, 머신러닝, 자연어처리, 딥러닝, 심층학습, 확률적 추론, 지능적 에이전트, 컴퓨터 비전
분   야 컴퓨터 공학 / 인공지능

 

관련 사이트

 아마존 도서소개 페이지

 저작권사 도서소개 페이지

 독자 Q&A(역자 홈페이지)

 

관련 포스트

 2021/8/12 - 인공신경망의 부활과 심층학습의 눈부신 성과를 반영한 인공지능 연구의 결정판!

 

관련 시리즈

 아이러브 A.I.(IA.I.)

 

관련 도서

 패턴 인식과 머신 러닝

 단단한 강화학습: 강화학습 기본 개념을 제대로 정리한 인공지능 교과서

 

관련 파일 다운로드

 예제 코드 다운로드

 컬러 그림 다운로드

■ 참고문헌:

인공지능(제4판) 전체 참고문헌.pdf
1.21MB

■ 1, 2권 전체 찾아보기:

인공지능(제4판) 전체 찾아보기.pdf
1.88MB

 

교재 검토용 증정 안내

■ 학교 및 학원에서 교재 선정을 위해 책을 파일로 검토해보고자 하시는 분들은 다음의 페이지에서 신청 양식을 작성해주시기 바랍니다. 확인 후 연락을 드리도록 하겠습니다. http://goo.gl/vBtPo3

 

강의보조 자료

■ 교재로 채택하신 분들은 메일(textbook@jpub.kr)을 보내주시면 본문의 그림과 표 자료를 보내드리겠습니다.

■ 전판인 3판까지는 책의 본문에 실렸던 연습문제가 4판에서는 원서부터가 웹 제공으로 바뀌었으나, 21년 8월 현재까지 완성되지 않은 상태여서 책에 번역하여 싣지 못한 연습문제 영문 링크를 다음과 같이 알려드립니다:

https://aimacode.github.io/aima-exercises/

 

샘플 PDF(표지, 차례, 옮긴이 머리말, 머리말, 각 권의 베타리더 후기, 1장 '소개', 19장 '견본에서 배우는 학습' 일부)

인공지능(제4판)_Sample.pdf
2.32MB

정오표 페이지

■ (등록되는 대로 링크를 걸어드리겠습니다)

 

도서구매 사이트(가나다순)

 [교보문고]  [도서11번가]  [알라딘]  [예스이십사]  [인터파크]  [쿠팡]  

 

도서 소개

전 세계 1,500개 이상의 대학에서 교재로 사용하는 인공지능 교과서!

 

‘인공지능 바이블’로 통하는 이 책은 이미 3판에서도 인공지능 연구의 결정판으로서의 위치를 확고히 한 바 있다. 최소 134개국 1,536개 이상의 대학에서 교재로 사용하고 있으며(미국에서만 557개 대학에서 채택), 전 세계의 교육자들은 이 책을 다음과 같이 칭송하고 있다.

 

인공지능에 관한 책을 딱 하나만 가진다면, 그 책은 반드시 이 책이어야 한다. 이 책은 해박하고 상세하며, 흥미롭고 유용한 통찰로 가득하다. 

— 하버드대학교의 아비 페퍼 교수

 

이번에도 저자들은 훌륭한 교과서를 좀 더 훌륭한 교과서로 만드는 데 성공했다. 직관적이고 명확한 서술을 통해 복잡한 문제를 쉽게 만드는 영리한 글쓰기를 보여주었으며, 그것을 이토록 광범위한 주제에 적용했다는 점에 무한한 존경을 표한다. 나는 최근 인공지능에 관한 관심이 부활한 데에는 이런 뛰어난 교과서 덕분이라고 확신한다.

— 독일 다름슈타트 공과대학의 볼프강 비벨 교수

 

이 책은 인공지능의 최전선에 대한 확신과 낙관주의, 그리고 전염력 있는 흥분이 가득하다. 그러면서도 이 분야의 복잡성과 깊이를 전혀 손상하지 않았다. 이 책은 교수와 학생이 모두 반기고 즐길 책이다.

— 매사추세츠대학교 애머스트 캠퍼스의 슐로모 질버스틴 교수

 

러셀과 노빅의 제1판에 대한 나의 반응은 ‘지금까지 나온 최고의 책이긴 하지만 더 나은 책을 쓰는 게 불가능하지는 않겠다’였다. 제2판에 대한 반응은 ‘그 누구도 이보다 나은 인공지능 교과서를 쓰지는 못할 것이다’였다. 이 서평을 위해 제3판을 자세히 읽고 나니 그런 생각이 다시 확실해졌다. 이 책은 《The Feynman Lectures on Physics》(파인만의 물리학 강의) 수준의, 놀랄 만큼 깊고 넓은 훌륭한 교과서다. 그리고 이 책이 단지 인공지능 강좌를 위한 교재인 것은 아니다. 이 책은 합리적 사고와 행동의 이론에 관한 미증유의 개괄서이다.

— 뉴욕대학교의 어니 데이비스 교수

 

놀라운 성취이자 진정으로 아름다운 책!

— 렌셀러 폴리테크닉대학교의 셀머 브링스조드 교수

 

지은이 소개

스튜어트 러셀(Stuart Russell)
스튜어트 러셀은 현재 버클리 대학교의 컴퓨터과학과 교수이자 Center for Intelligent Systems의 책임자, 그리고 공학 스미스–자데 석좌교수(Smith–Zadeh Chair)이다. 또한, 인공지능의 다양한 주제에 관해 300편이 넘는 논문을 발표했으며, 그가 쓴 다른 책으로는 《The Use of Knowledge in Analogy and Induction》과 《Do the Right Thing: Studies in Limited Rationality》, 그리고 《Human Compatible: Artificial Intelligence and the Problem of Control》이 있다.

 

피터 노빅(Peter Norvig)
피터 노빅은 현재 구글의 연구실장이며, 2002년에서 2005년까지 핵심 웹 검색 엔진 개발을 이끌었다. 전에 NASA Ames Research Center의 계산 과학 분과장으로서 NASA의 인공지능 및 로봇공학 연구와 개발을 감독했다. 서던 캘리포니아 대학교의 교수였으며, 버클리 대학교와 스탠퍼드 대학교의 연구교수단 일원이었다. 그의 다른 책으로는 《Paradigms of AI Programming: Case Studies in Common Lisp》와 《Verbmobil: A Translation System for Face–to–Face Dialog》, 그리고 《Intelligent Help Systems for UNIX》가 있다.

 

두 저자는 2016 AAAI(American Association for Artifi cial Intelligence)/EAAI(Educational Advances in Artificial Intelligence)의 제1회 우수교육자(Outstanding Educator) 상을 공동 수상했다.

 

옮긴이 소개

류광

커누스 교수의 《컴퓨터 프로그래밍의 예술》 시리즈(한빛미디어)를 포함하여 80여 종의 다양한 IT 전문서를 번역한 전문 번역가이다. 본서 외에 《인공지능: 현대적 접근방식 제3판》(제이펍, 2016), 《심층 학습》(제이펍, 2018), 《신경망과 심층 학습》(제이펍, 2019), 《파이썬으로 배우는 자연어 처리 인 액션》(제이펍, 2020), 《심층 강화학습 인 액션》(제이펍, 2020) 같은 인공지능 관련 서적을 번역했다.

 

차례

PART V 기계학습

CHAPTER 19 견본에서 배우는 학습 ∙ 3

19.1 학습의 여러 형태 ······················································································· 4

19.2 지도학습 ····································································································· 6

19.3 결정 트리의 학습 ····················································································· 11

19.4 모형 선택과 최적화 ················································································· 21

19.5 학습 이론 ································································································· 30

19.6 선형 회귀와 분류 ····················································································· 35

19.7 비매개변수 모형 ······················································································· 47

19.8 앙상블 학습 ······························································································ 59

19.9 기계학습 시스템 개발 ·············································································· 69

‣‣ 요약 ···································································································· 81

‣‣ 참고문헌 및 역사적 참고사항 ··························································· 82

더보기

CHAPTER 20 확률 모형의 학습 ∙ 89

20.1 통계적 학습 ······························································································ 90

20.2 완전 데이터를 이용한 학습 ····································································· 93

20.3 은닉 변수가 있는 학습: EM 알고리즘 ················································· 109

‣‣ 요약 ·································································································· 119

‣‣ 참고문헌 및 역사적 참고사항 ························································· 120

 

CHAPTER 21 심층학습 ∙ 125

21.1 단순 순방향 신경망 ··············································································· 127

21.2 심층학습을 위한 계산 그래프 ······························································· 133

21.3 합성곱 신경망 ························································································ 137

21.4 학습 알고리즘 ························································································ 144

21.5 일반화 ····································································································· 148

21.6 순환 신경망 ···························································································· 153

21.7 비지도학습과 전이학습 ·········································································· 157

21.8 응용 ········································································································ 165

‣‣ 요약 ·································································································· 168

‣‣ 참고문헌 및 역사적 참고사항 ························································· 168

 

CHAPTER 22 강화학습 ∙ 173

22.1 보상 기반 학습 ······················································································ 173

22.2 수동 강화학습 ························································································ 176

22.3 능동 강화학습 ························································································ 183

22.4 강화학습의 일반화 ················································································· 191

22.5 정책 검색 ······························································································· 199

22.6 견습 학습과 역강화학습 ········································································ 202

22.7 강화학습의 응용 ····················································································· 206

‣‣ 요약 ·································································································· 209

‣‣ 참고문헌 및 역사적 참고사항 ························································· 211

 

PART VI 의사소통, 지각, 행동

CHAPTER 23 자연어 처리 ∙ 217

23.1 언어 모형 ······························································································· 218

23.2 문법 ········································································································ 231

23.3 파싱 ········································································································ 233

23.4 증강 문법 ······························································································· 240

23.5 실제 자연어의 복잡한 사항들 ······························································· 246

23.6 자연어 처리 과제들 ··············································································· 250

‣‣ 요약 ·································································································· 252

‣‣ 참고문헌 및 역사적 참고사항 ························································· 253

 

CHAPTER 24 자연어 처리를 위한 심층학습 ∙ 259

24.1 단어 내장 ······························································································· 260

24.2 NLP를 위한 순환 신경망 ······································································ 264

24.3 순차열 대 순차열 모형 ·········································································· 268

24.4 트랜스포머 구조 ····················································································· 274

24.5 사전훈련과 전이학습 ·············································································· 277

24.6 현황 ········································································································ 282

‣‣ 요약 ·································································································· 285

‣‣ 참고문헌 및 역사적 참고사항 ························································· 285

 

CHAPTER 25 컴퓨터 시각 ∙ 289

25.1 소개 ········································································································ 289

25.2 이미지 형성 ···························································································· 291

25.3 단순 이미지 특징 ··················································································· 298

25.4 이미지 분류 ···························································································· 306

25.5 물체 검출 ······························································································· 311

25.6 3차원 세계 ····························································································· 314

25.7 컴퓨터 시각의 용도 ··············································································· 319

‣‣ 요약 ·································································································· 334

‣‣ 참고문헌 및 역사적 참고사항 ························································· 335

 

CHAPTER 26 로봇공학 ∙ 341

26.1 로봇 ······································································································ 341

26.2 로봇 하드웨어 ······················································································ 342

26.3 로봇공학이 푸는 문제들 ······································································ 347

26.4 로봇 지각 ····························································································· 349

26.5 계획 수립과 제어 ················································································· 357

26.6 불확실한 운동의 계획 ·········································································· 378

26.7 로봇공학의 강화학습 ············································································ 381

26.8 인간과 로봇 ·························································································· 384

26.9 로봇공학의 또 다른 틀 ········································································ 394

26.10 응용 영역 ····························································································· 397

‣‣ 요약 ································································································ 400

‣‣ 참고문헌 및 역사적 참고사항 ······················································· 402

 

PART VII 결론

CHAPTER 27 인공지능의 철학, 윤리학, 안전 ∙ 411

27.1 인공지능의 한계 ····················································································· 411

27.2 기계가 정말로 생각할 수 있을까? ······················································· 416

27.3 인공지능의 윤리 ····················································································· 418

‣‣ 요약 ·································································································· 443

‣‣ 참고문헌 및 역사적 참고사항 ························································· 443

 

CHAPTER 28 인공지능의 미래 ∙ 451

28.1 인공지능의 구성요소 ·············································································· 452

28.2 인공지능 구조 ························································································ 459

 

APPENDIX A 수학적 배경 ∙ 465

A.1 복잡도 분석과 O( ) 표기법 ··································································· 465

A.2 벡터, 행렬, 선형대수 ············································································· 468

A.3 확률분포 ································································································· 470

‣‣ 참고문헌 및 역사적 참고사항 ························································· 473

 

APPENDIX B 언어와 알고리즘에 관해 ∙ 475

B.1 BNF를 이용한 언어의 정의 ·································································· 475

B.2 알고리즘 서술에 쓰이는 의사코드 ························································ 476

B.3 온라인 보조 자료 ··················································································· 478

 

• 참고문헌 ·················································· 479

• 찾아보기 ·················································· 537

 

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