| 1. | |
인공지능 교과목 소개 | Course overview, course textbook, course topics |  |
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01-인공지능 교과목 소개 |  |
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| 2. | |
인공지능 개요 | What is AI?, Can Machines Act/Think Intelligently?, Main Areas of AI, AI History | |
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02-인공지능 개요 | |
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| 3. | |
Intelligent Agents | What Is an Agent?, Rational Agents, How Is an Agent Different from Other Software?, PEAS, Environment Types, Agent Types | |
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03-Intelligent Agents | |
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| 4. | |
Logical Agents - Part 1 | Knowledge and Reasoning, Knowledge-based agents, Knowledge representation, Knowledge base, Wumpus World | |
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04-Logical Agents - Part 1 | |
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| 5. | |
Logical Agents - Part 2 | Logic in General, Propositional Logic, Entailment, Inference, Proof Methods, Normal Clausal Form | |
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05-Logical Agents - Part 2 | |
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| 6. | |
First-Order Logic | First-order logic syntax, quantifiers, Translating English to FOL, Using FOL | |
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06-First-Order Logic | |
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| 7. | |
Inference in FOL - Part 1 | Inference with Quantifiers, Reduction to propositional inference, Unification, Generalized Modus Ponens (GMP) | |
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07-Inference in FOL - Part 1 | |
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| 8. | |
Inference in FOL - Part 2 | Inference Methods, Forward Chaining, Backward Chaining | |
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08-Inference in FOL - Part 2 | |
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| 9. | |
Inference in FOL - Part 3 | Conversion to CNF, Resolution, Resolution Example | |
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09-Inference in FOL - Part 3 | |
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| 10. | |
Uncertainty | Uncertain Agent, Types of Uncertainty, How do we deal with uncertainty?, How do we represent uncertainty?, Probability, Prior probability, Conditional probability, Independence | |
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10-Uncertainty | |
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| 11. | |
Probabilistic Reasoning | Computing with Probabilities, Conditional Independence, Bayesian Networks, Examples of Simple Bayesian Networks, Inference (Reasoning) in Bayesian Networks | |
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11-Probabilistic Reasoning | |
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| 12. | |
Learning | Learning, Types of Learning, Inductive Learning Method | |
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12-Learning | |
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| 13. | |
Decision Trees | Learning Decision Trees, Attribute-based Representations, Finding Compact Decision Trees, Choosing an Attribute, Information Gain | |
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13-Decision Trees | |
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