├── Lab ├── AI基础实验报告（模板）.doc ├── Assignment_1 │ └── Assignment1.pdf ├── Assignment_2 │ └── Assignment2.pdf ├── Assignment_3 │ └── Assignment3.pdf ├── Assignment_4 │ └── Assignment4.pdf ├── Project_1 │ ├── Prob1 │ │ ├── 1_1.py │ │ ├── 1_2.py │ │ └── 1_3.py │ ├── Prob2 │ │ ├── 2_1.py │ │ ├── 2_2.py │ │ ├── 2_3.py │ │ └── 2_4.py │ ├── Prob3 │ │ └── maze_visualization.py │ ├── Project1_APP.pdf │ ├── Project1_Code.pdf │ ├── Project1_readme.pdf │ └── Warm_Up.pdf ├── Project_2 │ ├── Project Two.pdf │ └── Quickstart.ipynb └── 课程总体要求.pptx ├── Readme.md ├── figure └── AI.jpg ├── lecture01 └── lec01.pdf ├── lecture02 └── lec02.pdf ├── lecture03 └── lec03.pdf ├── lecture04 └── lec04.pdf ├── lecture05 └── lec05.pdf ├── lecture06 └── lec06.pdf ├── lecture07 └── lec07.pdf ├── lecture08 └── lec08.pdf ├── lecture09 └── lec09.pdf ├── lecture10 └── lec10.pdf ├── lecture11 └── lec11.pdf ├── lecture12 └── lec12.pdf ├── lecture13 ├── lec11-3-Tree-Model.pdf ├── lec12-1-Basics-of-DL.pdf └── lec12-2-MLP.pdf ├── lecture14 └── lec12-3-CNN.pdf ├── lecture15 └── lec12-4-RNN&Transformer.pdf └── lecture16 └── lecc13-CV.pdf /Lab/AI基础实验报告（模板）.doc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Philosober/AI-fundamentals-2025-Spring/a623db8243625306bfbd47c46726ac792127d6ef/Lab/AI基础实验报告（模板）.doc -------------------------------------------------------------------------------- /Lab/Assignment_1/Assignment1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Philosober/AI-fundamentals-2025-Spring/a623db8243625306bfbd47c46726ac792127d6ef/Lab/Assignment_1/Assignment1.pdf -------------------------------------------------------------------------------- /Lab/Assignment_2/Assignment2.pdf: -------------------------------------------------------------------------------- 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minor=True) 19 | plt.gca().set_yticks([y - 0.5 for y in range(1, len(maze))], minor=True) 20 | plt.grid(which="minor", color="black", linestyle='-', linewidth=2) 21 | 22 | plt.axis('on') # 显示坐标轴 23 | plt.show() 24 | 25 | # 提供迷宫的二维数组 26 | maze = [ 27 | [0, 1, 0, 0, 0], 28 | [0, 1, 0, 1, 0], 29 | [0, 0, 0, 0, 0], 30 | [0, 1, 1, 1, 0], 31 | [0, 0, 0, 1, 0] 32 | ] 33 | 34 | # 假设给定路径的坐标列表 35 | path = [(0, 0), (1, 0), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (3, 4), (4, 4)] 36 | 37 | # 可视化迷宫及路径 38 | visualize_maze_with_path(maze, path) 39 | -------------------------------------------------------------------------------- /Lab/Project_1/Project1_APP.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Philosober/AI-fundamentals-2025-Spring/a623db8243625306bfbd47c46726ac792127d6ef/Lab/Project_1/Project1_APP.pdf -------------------------------------------------------------------------------- /Lab/Project_1/Project1_Code.pdf: 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/Lab/Project_2/Project Two.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Philosober/AI-fundamentals-2025-Spring/a623db8243625306bfbd47c46726ac792127d6ef/Lab/Project_2/Project Two.pdf -------------------------------------------------------------------------------- /Lab/课程总体要求.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Philosober/AI-fundamentals-2025-Spring/a623db8243625306bfbd47c46726ac792127d6ef/Lab/课程总体要求.pptx -------------------------------------------------------------------------------- /Readme.md: -------------------------------------------------------------------------------- 1 | # AI Fundamentals 2025 Spring 2 | 3 | （ECNU-DaSE）AI Fundamentals 2025 Spring 4 | 5 | 本仓库为《人工智能基础》（拔尖基地班）2025年春季学期课程仓库。所有的课程安排、资料、习题、作业、项目等均放在此处。欢迎大家积极提出问题和建议，一起来交流~ 6 | 7 |
8 | 9 | 10 | 11 |
12 | 13 | 14 | 15 | ### 1、课程说明 16 | 17 | ​ 随着Web2.0、云计算和大数据技术的普及，人工智能（AI）技术也得到了快速发展迅速。如今人工智能技术代表国际战略新高度，其具有广阔的应用前景与巨大的需求空间，众多的智能系统已经真正地从理论研究走向了实践应用。经过几十年的发展，人工智能已具备了丰富的内涵和广阔的外延。大数据和人工智能是从不同角度发展起来的学科领域，两者之间相辅相成、并相互融合促进，人工智能为大数据管理和分析提供算法基础，大数据为人工智能提供数据基础，并促进了人工智能技术的快速发展。人工智能技术成为了数据科学与工程专业人才必须掌握的核心技术。面向不同的应用、站在不同的角度，不同人对人工智能有不同的理解。总体来说，“数据-知识-服务”是大数据研究与应用的基本框架，在该框架的语境下围绕知识获取和表示、知识挖掘和学习、知识推理和决策开设本门课程，旨在为数据科学与大数据技术相关专业学生传递人工智能的基本理念，为数据驱动的知识发现与应用的全生命周期提供智能技术的算法基础，培养学生的数据分析能力和智能计算思维。 18 | 19 | ​ 本课程主要讲授主流的人工智能技术，包括人工智能简介，问题求解，知识，推理和规划，不确定性，机器学习，和AI应用。课程既体现人工智能的内涵，也体现数据驱动的知识工程的基本理念；既面向数据科学讲授人工智能领域的主流前沿技术，也介绍经典理论、模型和算法；既注重基础理论和经典算法，也注重实际案例与实践项目。通过本门课程的学习，数据科学与大数据技术专业的学生可以掌握人工智能的基本应用中所涉及的原理、经典方法和关键技术，为进一步学习人工智能理论与算法、应用人工智能原理和方法解决数据科学与工程领域内的实际问题奠定基础。 20 | 21 | ### 2、课程安排 22 | 23 | 详细的课程内容安排如下所示： 24 | 25 | --- 26 | 27 | 28 | | 周数📆 | 日期🕣 | 内容📒 | 主讲💂‍♂️ | 本周任务 📌 | 课件📘 | 29 | | :---: | :---: | ------------------------------------------------------------ | :------: | :----------------------------------------------------------: | :----------------------------------------------------------: | 30 | | 01 | 2.19 | Introduction and Intelligent Agents | Bin Yang | [Assignment1](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/Lab/Assignment_1/Assignment1.pdf) | [课件01](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture01/lec01.pdf) | 31 | | 02 | 2.26 | Problem Solving: Search | Bin Yang | [Project1](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/Lab/Project_1/Warm_Up.pdf) | [课件02](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture02/lec02.pdf) | 32 | | 03 | 3-5 | Problem Solving: Informed Search
and Search in Complex Environments | Bin Yang | [Project1_Code](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/Lab/Project_1/Project1_Code.pdf) / [Readme](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/Lab/Project_1/Project1_readme.pdf) | [课件03](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture03/lec03.pdf) | 33 | | 04 | 3-12 | Search in Complex Environments | Bin Yang | [Project1_APP](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/Lab/Project_1/Project1_APP.pdf) / [Readme](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/Lab/Project_1/Project1_readme.pdf) | [课件04](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture04/lec04.pdf) | 34 | | 05 | 3-19 | Adversarial Search and Games | Bin Yang | \ | [课件05](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture05/lec05.pdf) | 35 | | 06 | 3-26 | Constraint Satisfaction Problems | Bin Yang | \ | [课件06](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture06/lec06.pdf) | 36 | | 07 | 4-2 | Logical Agents | Bin Yang | \ | [课件07](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture07/lec07.pdf) | 37 | | 08 | 4-9 | Knowledge Representation | Bin Yang | \ | [课件08](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture08/lec08.pdf) | 38 | | 09 | 4-16 | Knowledge Representation & Automated Planning | Yang Shu | [Assignment2](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/Lab/Assignment_2/Assignment2.pdf) | [课件09](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture09/lec09.pdf) | 39 | | 10 | 4-23 | Quantifying Uncertainty | Yang Shu | \ | [课件09](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture09/lec09.pdf) | 40 | | 11 | 4-30 | Quantifying Uncertainty and Probabilistic Reasoning | Yang Shu | [Assignment3](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/Lab/Assignment_3/Assignment3.pdf) | [课件10](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture10/lec10.pdf) | 41 | | 12 | 5-7 | Introduction of Machine Learning | Yang Shu | [Project2 Quickstart](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/Lab/Project_2/Quickstart.ipynb) | [课件11](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture11/lec11.pdf) | 42 | | 13 | 5-14 | Linear Model | Yang Shu | [Project2](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/Lab/Project_2/Project%20Two.pdf) | [课件12](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture12/lec12.pdf) | 43 | | 14 | 5-21 | Tree Model & Intro-DL & MLP | Yang Shu | \ | [课件13](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture13/) | 44 | | 15 | 5-28 | CNN Model | Yang Shu | \ | [课件14](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture14/) | 45 | | 16 | 6-4 | RNN&Transformer | Yang Shu | \ | [课件15](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture15/) | 46 | | 17 | 6-11 | Computer Vision | Yang Shu | \ | [课件16](https://github.com/Philosober/AI-fundamentals-2025-Spring/blob/main/lecture16/) | 47 | | | | | | | | 48 | 49 | ### 3、课程考核 50 | 51 | 52 | 53 | ### 4、参考教材 54 | 55 | [1] Stuart J.Russell，Peter Norvig，《人工智能：一种现代的方法（第3版 影印版）》，北京：清华大学出版社，2011年。（主教材） 56 | 57 | [2] Avrim Blum, John Hopcroft, Ravi Kannan，Foundations of Data Science，Cambridge University Press，2018年。 58 | 59 | [3] 史忠植著，《人工智能》，北京：机械工业出版社，2016年。 60 | 61 | [4] 蔡自兴，刘丽珏，蔡竞峰，陈白帆 著，《人工智能及其应用（第5版）》，北京：清华大学出版社，2016年。 62 | 63 | [5] [澳] Michael Negnevitsky 著，人工智能：智能系统指南（英文版·第3版），北京：机械工业出版社，2011年。 64 | 65 | [6] [美] Nils J. Nilsson著，Artificial Intelligence A New Synthesis，北京：机械工业出版社，1999年。 66 | 67 | [7] 周志华，《机器学习》，北京：清华大学出版社，2016年。 68 | -------------------------------------------------------------------------------- /figure/AI.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Philosober/AI-fundamentals-2025-Spring/a623db8243625306bfbd47c46726ac792127d6ef/figure/AI.jpg -------------------------------------------------------------------------------- /lecture01/lec01.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Philosober/AI-fundamentals-2025-Spring/a623db8243625306bfbd47c46726ac792127d6ef/lecture01/lec01.pdf -------------------------------------------------------------------------------- /lecture02/lec02.pdf: -------------------------------------------------------------------------------- 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