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2016년에 나온 제3판 번역서(2009년에 출간된 원서 3판을 번역)는 최신 연구 반영에 한계가 있었으나, 이번 제4판 번역서는 최근 성과(자연어 이해, 로봇공학, 컴퓨터 시각에 심층학습이 끼친 영향, 강화학습을 로봇공학에 적용하는 방법, 기계학습, 인공지능 윤리 등)를 충실하게 반영하였다. ?
저자: 스튜어트 러셀, 피터 노빅 (지은이), 류광 (옮긴이)
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
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
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
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
2010년대 인공신경망의 부활과 심층학습의 눈부신 성과를 반영한 인공지능 연구의 결정판!
2016년에 나온 제3판 번역서(2009년에 출간된 원서 3판을 번역)는 최신 연구 반영에 한계가 있었으나, 이번 제4판 번역서는 최근 성과(자연어 이해, 로봇공학, 컴퓨터 시각에 심층학습이 끼친 영향, 강화학습을 로봇공학에 적용하는 방법, 기계학습, 인공지능 윤리 등)를 충실하게 반영한, 2020년에 출간된 원서를 옮긴 것이라 이 분야의 ‘좀 더 통합된 상’을 원하는 여러 독자의 갈증을 해소하는 데 큰 도움이 될 것입니다.
전체적으로, 책의 약 25%가 완전히 새로운 내용이고 나머지 75%도 이 분야의 좀 더 통합된 상을 제시하기 위해 크게 변경되었으며, 이번 판에서 인용한 문헌의 22%는 2010년 이후에 출판된 것들입니다.
제4판에서 새로운 점들
■ 사람이 손으로 짜는 지식 공학보다는 기계학습에 좀 더 무게를 실었다. 기계학습은 가용 데이터와 컴퓨팅 자원이 증가하고 새로운 알고리즘들이 등장한 덕분에 큰 성공을 거두고 있다.
■ 심층학습, 확률적 프로그래밍, 다중 에이전트 시스템을 각각 개별적인 장(챕터)으로 두어서 좀 더 자세히 다룬다.
■ 자연어 이해, 로봇공학, 컴퓨터 시각에 관한 장들을 심층학습이 끼친 영향을 반영해서 수정했다.
■ 로봇공학 장에 사람과 상호작용하는 로봇에 관한 내용과 강화학습을 로봇공학에 적용하는 방법에 관한 내용이 추가되었다.
■ 이전에는 인공지능의 목표를 사람이 구체적인 효용 정보(목적함수)를 제공한다는 가정하에서 기대 효용을 최대화하려는 시스템을 만드는 것이라고 정의했다. 그러나 이번 판에서는 목적함수가 고정되어 있으며 인공지능 시스템이 목적함수를 알고 있다고 가정하지 않는다. 대신, 시스템은 자신이 봉사하는 인간의 진짜 목적이 무엇인지 확실하게 알지 못할 수 있다고 가정한다. 시스템은 반드시 자신이 무엇을 최대화할 것인지를 배워야 하며, 목적에 관해 불확실성이 존재하더라도 적절히 작동해야 한다.
■ 인공지능이 사회에 미치는 영향을 좀 더 자세하게 다루었다. 여기에는 윤리, 공정성, 신뢰, 안정성에 관한 핵심적인 문제들을 고찰한다.
■ 각 장 끝의 연습문제들을 온라인 사이트로 옮겼다. 덕분에 강사들의 요구와 이 분야 및 인공지능 관련 소프트웨어 도구의 발전에 맞게 연습문제들을 계속 추가, 갱신, 개선할 수 있게 되었다.
2016년에 나온 제3판 번역서(2009년에 출간된 원서 3판을 번역)는 최신 연구 반영에 한계가 있었으나, 이번 제4판 번역서는 최근 성과(자연어 이해, 로봇공학, 컴퓨터 시각에 심층학습이 끼친 영향, 강화학습을 로봇공학에 적용하는 방법, 기계학습, 인공지능 윤리 등)를 충실하게 반영한, 2020년에 출간된 원서를 옮긴 것이라 이 분야의 ‘좀 더 통합된 상’을 원하는 여러 독자의 갈증을 해소하는 데 큰 도움이 될 것입니다.
전체적으로, 책의 약 25%가 완전히 새로운 내용이고 나머지 75%도 이 분야의 좀 더 통합된 상을 제시하기 위해 크게 변경되었으며, 이번 판에서 인용한 문헌의 22%는 2010년 이후에 출판된 것들입니다.
제4판에서 새로운 점들
■ 사람이 손으로 짜는 지식 공학보다는 기계학습에 좀 더 무게를 실었다. 기계학습은 가용 데이터와 컴퓨팅 자원이 증가하고 새로운 알고리즘들이 등장한 덕분에 큰 성공을 거두고 있다.
■ 심층학습, 확률적 프로그래밍, 다중 에이전트 시스템을 각각 개별적인 장(챕터)으로 두어서 좀 더 자세히 다룬다.
■ 자연어 이해, 로봇공학, 컴퓨터 시각에 관한 장들을 심층학습이 끼친 영향을 반영해서 수정했다.
■ 로봇공학 장에 사람과 상호작용하는 로봇에 관한 내용과 강화학습을 로봇공학에 적용하는 방법에 관한 내용이 추가되었다.
■ 이전에는 인공지능의 목표를 사람이 구체적인 효용 정보(목적함수)를 제공한다는 가정하에서 기대 효용을 최대화하려는 시스템을 만드는 것이라고 정의했다. 그러나 이번 판에서는 목적함수가 고정되어 있으며 인공지능 시스템이 목적함수를 알고 있다고 가정하지 않는다. 대신, 시스템은 자신이 봉사하는 인간의 진짜 목적이 무엇인지 확실하게 알지 못할 수 있다고 가정한다. 시스템은 반드시 자신이 무엇을 최대화할 것인지를 배워야 하며, 목적에 관해 불확실성이 존재하더라도 적절히 작동해야 한다.
■ 인공지능이 사회에 미치는 영향을 좀 더 자세하게 다루었다. 여기에는 윤리, 공정성, 신뢰, 안정성에 관한 핵심적인 문제들을 고찰한다.
■ 각 장 끝의 연습문제들을 온라인 사이트로 옮겼다. 덕분에 강사들의 요구와 이 분야 및 인공지능 관련 소프트웨어 도구의 발전에 맞게 연습문제들을 계속 추가, 갱신, 개선할 수 있게 되었다.
