About this Course

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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

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Beginner Level

Approx. 18 hours to complete

Chinese (Traditional)

Skills you will gain

Artificial Intelligence (AI)
Search Algorithm
A.I. Artificial Intelligence
Algorithms

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Beginner Level

Approx. 18 hours to complete

Chinese (Traditional)

Instructor

Instructor rating

4.48/5 (31 Ratings)

于天立

副教授 (Associate Professor)

電機工程學系 (Department of Electrical Engineering)

26,806 Learners

2 Courses

Syllabus - What you will learn from this course

Content Rating95%(1,040 ratings)

Week1

Week 1

2 hours to complete

Introduction

2 hours to complete

9 videos (Total 118 min), 1 reading

9 videos

1-1 History of AI：TuringTest and Its Application, Chinese Room Argument5m1-2 What is AI16m1-3 Agents and Environments, PEAS, Environment Type19m1-4 Different Level Of AI5m1-5 Wave of AI：Debut, Knowledge Driven, Data Driven6m1-6 The Classification of Agent, First Wave of AI (Artificial Neural Network)24m1-7 Second Wave of AI (Expert System)12m1-8 Third Wave of AI (Some Theory and Principle of Machine Learning)21m1-9 Conclusion of AI and Machine Learning7m

1 reading

NTU MOOC 課程問題詢問與回報機制1m

Week2

Week 2

2 hours to complete

Uninformed search

2 hours to complete

6 videos (Total 102 min)

6 videos

2-1 Problem Solving Agents, Problem Formulation (i)14m2-2 Problem Formulation (ii) - Abstraction18m2-3 Search on Tree and Graph21m2-4 Uninformed Search (i) - Breadth-First Search, Uniform-Cost Search18m2-5 Uninformed Search (ii) - Depth-First Search, Depth-Limited Search, Iterative-Deepening Search14m2-6 Uninformed Search (iii) - Iterative-Deepening Search, Bidirectional Search15m

1 practice exercise

Week 230m

Week3

Week 3

2 hours to complete

Informed search

2 hours to complete

6 videos (Total 108 min)

Week4

Week 4

2 hours to complete

Non-classic search

2 hours to complete

7 videos (Total 119 min)

Week5

Week 5

2 hours to complete

Adversarial search

2 hours to complete

6 videos (Total 118 min)

Week6

Week 6

2 hours to complete

Propositional Logic

2 hours to complete

7 videos (Total 115 min)

7 videos

6-1 Logical Agents (i) - Generic Knowledge-Based Agent, PEAS16m6-2 Logical Agents (ii) - Logic, Entailment and Models21m6-3 Propositional Logic, Inference (i) - Enumeration, Validity and Satisfiability19m6-4 Inference (ii) - Simple Knowledge, Resolution and CNF (i) - Proof by Resolution, CNF Conversion, Resolution Algorithm18m6-5 Resolution and CNF (ii) - Properties of Resolution, Ground Resolution Theorem11m6-6 Resolution and CNF (iii) - Horn and Definite Clauses, Forward Chaining17m6-7 Backward Chaining, Pros and Cons of Propositional Logic10m

1 practice exercise

Week 630m

Week7

Week 7

3 hours to complete

First Order Logic

3 hours to complete

8 videos (Total 127 min)

8 videos

7-1 First-Order Logic (i) - Syntax of FOL and Semantics16m7-2 First-Order Logic (ii) - Using FOL, Inference (i) - Instantiation13m7-3 Inference (ii) - Propositionalization, Inference (iii) - Unification18m7-4 Inference (iii) - Unification, Inference (iv) - Forward chaining18m7-5 Inference (iv) - Forward chaining, Inference (v) - Backward chaining13m7-6 Logic Programing (i) - Prolog Systems17m7-7 Logic Programing (ii) - Redundant Inference and Infinite Loops in Prolog12m7-8 Inference (vi) - Resolution16m

1 practice exercise

Week 730m

Week8

Week 8

3 hours to complete

Planning

3 hours to complete

6 videos (Total 129 min), 1 reading, 1 quiz

Reviews

4.8

57 reviews

5 stars
82.77%
4 stars
14.23%
3 stars
2.62%
2 stars
0.37%

TOP REVIEWS FROM 人工智慧：搜尋方法與邏輯推論 (ARTIFICIAL INTELLIGENCE - SEARCH & LOGIC)

by JYJan 23, 2017

上完這個課程，不但讓我對人工智慧領域有概括性的認識，且對各種搜尋或邏輯判斷的演算法優缺點均有概念。非常推薦給想要進入人工智慧領域的朋友來上這門課程。但建議至少要有一點計算機概論或演算法的基礎比較好喔！

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人工智慧：搜尋方法與邏輯推論 (Artificial Intelligence - Search & Logic)