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課程名稱 |
人工智慧
Artificial Intelligence |
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開課學期 |
109-2 |
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授課對象 |
學程 智慧醫療學分學程 |
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授課教師 |
許永真 |
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課號 |
CSIE5400 |
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課程識別碼 |
922EU3020 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期一7,8,9(14:20~17:20) |
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上課地點 |
資102 |
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備註 |
本課程以英語授課。智慧醫療學分學程所屬電資學院「數據領域課程」
總人數上限：90人 |
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課程網頁 |
https://course.agent.csie.ntu.edu.tw/ |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
Introduction to Artificial Intelligence (CSIE 5400)
Instructor: Prof. Jane Yung-jen Hsu （許永真教授）
TA1: Erick Chandra
TA2: 陳柏均 (Bert Chen)
Office: R318 (Prof. Jane), R344 (laboratory)
Email to Professor: yjhsu@csie.ntu.edu.tw
Email to TAs: aita2019s@agent.csie.ntu.edu.tw
Website: https://iagentntu.github.io/
Classroom: CSIE R102
Class schedule: Thursdays, 14:20-17:20
Course website: https://course.agent.csie.ntu.edu.tw/ |
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課程目標 |
This course will provide a broad understanding of basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems. The students will learn the theory, algorithms, and their applications.
Course coverage:
PART I | Introduction + Problem Solving and Search
- Chapter 1: Introduction to AI, history of AI
- Chapter 2: Intelligent agents
- Chapter 3: Uninformed search, heuristic search, A* algorithm
- Chapter 4: Beyond classical search
- Chapter 5: Adversarial search, games
- Chapter 6: Constraint Satisfaction Problems
PART II | Data-Driven AI
- Machine Learning: Basic concepts
- Chapter 18: Learning from examples
- Linear models: linear regression, perceptron, K-nearest neighbors
- Decision trees
- Statistical machine learning: Support Vector Machines
- Neural networks
PART III | Decision Making
- Chapter 7: Logical agents
- Chapter 13: Quantifying uncertainty
- Chapter 14: Bayesian networks
- Markov Decision Process
- Chapter 21: Reinforcement Learning
PART IV | Advanced Topics
- Natural Language Processing
- Computer Vision
- Robotics |
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課程要求 |
Algorithms, Python 2.7 programming language |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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參考書目 |
Russell, S. and Norvig, P. Artificial Intelligence: A Modern Approach, 3rd ed.
Pearson Education/Prentice Hall, 2010. ISBN-13:978-0-13-
604259-4 |
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評量方式
(僅供參考) |
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週次 |
日期 |
單元主題 |
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Week 1 |
2/22 |
Course Overview
Chapter 1: Introduction |
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Week 2 |
3/1 |
No lecture due to 228 Memorial
[DUE] Assignment #0 |
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Week 3 |
3/8 |
Chapter 2: Intelligent Agents
Chapter 3: Solving Problems by Searching |
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Week 4 |
3/15 |
Chapter 4: Search in Complex Environments
Chapter 6: Constraint Satisfaction Problems |
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Week 5 |
3/22 |
Chapter 5: Adversarial Search and Games
MCTS and AlphaGO |
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Week 6 |
3/29 |
Chapter 12: Quantifying Uncertainty
Chapter 13: Probabilistic Reasoning: Bayes Nets |
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Week 7 |
4/5 |
[NTU] Spring Break |
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Week 8 |
4/12 |
Chapter 14: Probabilistic Reasoning over Time |
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Week 9 |
4/19 |
Midterm |
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Week 10 |
4/26 |
Chapter 15: Probabilistic Programming
Chapter 16 Making Simple Decisions |
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Week 11 |
5/3 |
Chapter 19: Learning from Examples |
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Week 12 |
5/10 |
Chapter 20: Learning Probabilistic Models |
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Week 13 |
5/17 |
Chapter 21: Deep Learning |
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Week 14 |
5/24 |
Chapter 22: Reinforcement Learning |
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Week 15 |
5/31 |
Chapter 23: Natural Language Processing
Chapter 24: Deep Learning for Natural Language Processing |
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Week 16 |
6/7 |
Chapter 27: Philosophy, Ethics, and Safety of AI
Chapter 28: The Future of AI |
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Week 17 |
6/14 |
No lecture due to Dragon Boat Festival |
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Week 18 |
6/21 |
Final Exam |
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