À propos de ce cours : 本課程分為人工智慧（上）、人工智慧（下）兩部份，第一部分除了人工智慧概論外，著重在目標搜尋、meta heuristic、電腦對弈、演繹學習（包含證言邏輯、一階邏輯及 planning ）等技術。這些技術主要發展時機為人工智慧的第一波及第二波熱潮，也就是 1950 年代至 1990 年代附近的主流發展，即使到現在也在各個領域廣為應用。 課程教學目標： 使同學對人工智慧有基礎概念 同學能夠理解如何運用目標搜尋技術及演繹學習方式達成人工智慧 同學能將相關技術應用到自己的問題上
Les destinataires de ce cours : 具有基礎電腦知識與基礎邏輯觀念的普羅社會大眾。
Créé par : National Taiwan University
Enseigné par : 于天立, 副教授（Associate Professor）
電機工程學系(Electrical Engineering)
| Niveau | Débutant |
| Langue | Chinese (Traditional) |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
Introduction
9 vidéos, 1 lecture
- Reading: NTU MOOC 課程問題詢問與回報機制
- Vidéo: 1-2 What is AI
- Vidéo: 1-3 Agents and Environments, PEAS, Environment Type
- Vidéo: 1-4 Different Level Of AI
- Vidéo: 1-5 Wave of AI：Debut, Knowledge Driven, Data Driven
- Vidéo: 1-6 The Classification of Agent, First Wave of AI (Artificial Neural Network)
- Vidéo: 1-7 Second Wave of AI (Expert System)
- Vidéo: 1-8 Third Wave of AI (Some Theory and Principle of Machine Learning)
- Vidéo: 1-9 Conclusion of AI and Machine Learning
SEMAINE 2
Uninformed search
6 vidéos
- Vidéo: 2-2 Problem Formulation (ii) - Abstraction
- Vidéo: 2-3 Search on Tree and Graph
- Vidéo: 2-4 Uninformed Search (i) - Breadth-First Search, Uniform-Cost Search
- Vidéo: 2-5 Uninformed Search (ii) - Depth-First Search, Depth-Limited Search, Iterative-Deepening Search
- Vidéo: 2-6 Uninformed Search (iii) - Iterative-Deepening Search, Bidirectional Search
Noté: Week 2
SEMAINE 3
Informed search
6 vidéos
- Vidéo: 3-2 Best-First Search (ii) - A* Search
- Vidéo: 3-3 Best-First Search (iii) - Optimality of A*
- Vidéo: 3-4 Memory Bounded Search (i) - Iterative Deepening A*, RBFS
- Vidéo: 3-5 Memory Bounded Search (ii) - RBFS, Simplified Memory-bounded A*
- Vidéo: 3-6 Heuristic - Preformance, Generating Heuristics
Noté: Week 3
SEMAINE 4
Non-classic search
7 vidéos
- Vidéo: 4-2 Steepest Descent
- Vidéo: 4-3 Simulated Annealing
- Vidéo: 4-4 Evolutionary Computation
- Vidéo: 4-5 Non-deterministic Actions - AND-OR Search, Partial Observations (i) - Sensor-less
- Vidéo: 4-6 Partial Observations (ii) - With Sensors
- Vidéo: 4-7 Partial Observations (iii) - Unknown Environments
Noté: Week 4
SEMAINE 5
Adversarial search
6 vidéos
- Vidéo: 5-2 Optimal Decision, Negamax Search , Alpha-Beta Pruning (i)
- Vidéo: 5-3 Alpha-Beta Pruning (ii)
- Vidéo: 5-4 Asperasion Windows, NegaScout
- Vidéo: 5-5 Imperfect Decisions, Forward Pruning
- Vidéo: 5-6 Stochastic Games, Partially Observable Games
Noté: Week 5
SEMAINE 6
Propositional Logic
7 vidéos
- Vidéo: 6-2 Logical Agents (ii) - Logic, Entailment and Models
- Vidéo: 6-3 Propositional Logic, Inference (i) - Enumeration, Validity and Satisfiability
- Vidéo: 6-4 Inference (ii) - Simple Knowledge, Resolution and CNF (i) - Proof by Resolution, CNF Conversion, Resolution Algorithm
- Vidéo: 6-5 Resolution and CNF (ii) - Properties of Resolution, Ground Resolution Theorem
- Vidéo: 6-6 Resolution and CNF (iii) - Horn and Definite Clauses, Forward Chaining
- Vidéo: 6-7 Backward Chaining, Pros and Cons of Propositional Logic
Noté: Week 6
SEMAINE 7
First Order Logic
8 vidéos
- Vidéo: 7-2 First-Order Logic (ii) - Using FOL, Inference (i) - Instantiation
- Vidéo: 7-3 Inference (ii) - Propositionalization, Inference (iii) - Unification
- Vidéo: 7-4 Inference (iii) - Unification, Inference (iv) - Forward chaining
- Vidéo: 7-5 Inference (iv) - Forward chaining, Inference (v) - Backward chaining
- Vidéo: 7-6 Logic Programing (i) - Prolog Systems
- Vidéo: 7-7 Logic Programing (ii) - Redundant Inference and Infinite Loops in Prolog
- Vidéo: 7-8 Inference (vi) - Resolution
Noté: Week 7
SEMAINE 8
Planning
6 vidéos, 1 lecture
- Vidéo: 8-2 Planning Domain Definition Language (PDDL) (ii)
- Vidéo: 8-3 State-Space Search, Heuristics
- Vidéo: 8-4 Planning Graphs
- Vidéo: 8-5 GRAPHPLAN
- Vidéo: 8-6 Course Review
- Reading: 臺大開放式課程（NTU OpenCourseWare）：計算機概論
Noté: Week8
FAQ
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Créateurs
National Taiwan University
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Notation et examens
不錯
gj
For the most AI online courses, they usually tech you how to use the tool and build up into your system. insted of them, this course talk about the from AI concept and therory. For my preseonal, I like this like course than "how to use" course. It is a base, so that we can have a theory backgoud to support more advanced topic and build up our AI system. Besides, teacher always use a simple example to show the "what algorithm is" it makes us very easy to understand that. For example, I knew A* manay years ago but can not understand what is heristic h and he use a MRT as an example, then I finallly understand it. I'd recommend guys to choice the course if he/she wants to learn the AI
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老师授课十分有趣，引人入胜