-
- 주제분류
- 공학 >컴퓨터ㆍ통신 >컴퓨터공학
-
- 강의학기
- 2016년 2학기
-
- 조회수
- 38,992
-
- 평점
- 3.3/5.0 (3)
- 강의계획서
- 강의계획서
인공지능은 자율주행, 로봇, 음성인식, 얼굴/제스쳐 인식, 번역 등의 다양한 실생활 문제를 수학적/소프트웨어적 방법을 활요하여 해결하는 방법을 제시한다. 이 동영상 강좌는 수강생들이 실생활의 인공지능 문제에 대처할 수 있는 능력을 배양하는 것을 목표로 한다. 수강생들은 인공지능의 기본원리 및 인공지능 시스템 구현능력을 학습하게 된다.
- 수강안내 및 수강신청
- ※ 수강확인증 발급을 위해서는 수강신청이 필요합니다
차시별 강의
| 1. | ![]() |
인공지능 교과목 소개 | Course overview, course textbook, course topics | |
![]() |
01-인공지능 교과목 소개 | ![]() |
||
| 2. | ![]() |
인공지능 개요 | What is AI?, Can Machines Act/Think Intelligently?, Main Areas of AI, AI History | |
![]() |
02-인공지능 개요 | ![]() |
||
| 3. | ![]() |
Intelligent Agents | What Is an Agent?, Rational Agents, How Is an Agent Different from Other Software?, PEAS, Environment Types, Agent Types | |
![]() |
03-Intelligent Agents | ![]() |
||
| 4. | ![]() |
Logical Agents - Part 1 | Knowledge and Reasoning, Knowledge-based agents, Knowledge representation, Knowledge base, Wumpus World | |
![]() |
04-Logical Agents - Part 1 | ![]() |
||
| 5. | ![]() |
Logical Agents - Part 2 | Logic in General, Propositional Logic, Entailment, Inference, Proof Methods, Normal Clausal Form | |
![]() |
05-Logical Agents - Part 2 | ![]() |
||
| 6. | ![]() |
First-Order Logic | First-order logic syntax, quantifiers, Translating English to FOL, Using FOL | |
![]() |
06-First-Order Logic | ![]() |
||
| 7. | ![]() |
Inference in FOL - Part 1 | Inference with Quantifiers, Reduction to propositional inference, Unification, Generalized Modus Ponens (GMP) | |
![]() |
07-Inference in FOL - Part 1 | ![]() |
||
| 8. | ![]() |
Inference in FOL - Part 2 | Inference Methods, Forward Chaining, Backward Chaining | |
![]() |
08-Inference in FOL - Part 2 | ![]() |
||
| 9. | ![]() |
Inference in FOL - Part 3 | Conversion to CNF, Resolution, Resolution Example | |
![]() |
09-Inference in FOL - Part 3 | ![]() |
||
| 10. | ![]() |
Uncertainty | Uncertain Agent, Types of Uncertainty, How do we deal with uncertainty?, How do we represent uncertainty?, Probability, Prior probability, Conditional probability, Independence | |
![]() |
10-Uncertainty | ![]() |
||
| 11. | ![]() |
Probabilistic Reasoning | Computing with Probabilities, Conditional Independence, Bayesian Networks, Examples of Simple Bayesian Networks, Inference (Reasoning) in Bayesian Networks | |
![]() |
11-Probabilistic Reasoning | ![]() |
||
| 12. | ![]() |
Learning | Learning, Types of Learning, Inductive Learning Method | |
![]() |
12-Learning | ![]() |
||
| 13. | ![]() |
Decision Trees | Learning Decision Trees, Attribute-based Representations, Finding Compact Decision Trees, Choosing an Attribute, Information Gain | |
![]() |
13-Decision Trees | ![]() |
연관 자료











