2024-Artificial-intelligence-for-complex-systems

This is the source code and materials for Artificial intelligence for complex systems course in BNU, 2024 Spring.

Tencent Meeting

972-9682-7117

The links to the course recordings

录制：面向复杂系统的人工智能 日期：2024-02-22 13:22:19 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=e920ec51-de40-49d0-b5d9-e9e7fc05478a

录制：面向复杂系统的人工智能 日期：2024-02-29 13:09:19 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=edbdbbcc-2228-49ba-a7c7-f5de8961c8c5

录制：面向复杂系统的人工智能 日期：2024-03-07 13:12:38 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=382cfaf0-db9c-4280-986e-f703c9b5a78e

录制: 面向复杂系统的人工智能 日期: 2024-03-14 13:14:13 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=8c1f15ae-c7ad-4b96-b02d-9be6808d18b9&from=3&record_type=2

录制：面向复杂系统的人工智能 日期：2024-03-21 13:13:40 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=ba504fcb-ce9f-44d4-ab22-65e216382c0f&from=3&is-single=false&record_type=2

录制：面向复杂系统的人工智能 日期：2024-03-28 13:19:19 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=0bf8b654-bbf7-42fd-b048-7700935c5f9a&from=3&is-single=false&record_type=2

录制：面向复杂系统的人工智能 日期：2024-04-11 13:14:08 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=e5ec0aa1-a51d-45f6-8a03-24fda1fc4f14&from=3&is-single=false&record_type=2

录制：面向复杂系统的人工智能 日期：2024-04-18 13:22:15 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=684ba213-975e-49bd-84f5-e0aee5ab4c7c&from=3&is-single=false&record_type=2

录制：面向复杂系统的人工智能 日期：2024-04-25 13:14:16 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=8464acd7-ca68-430d-939a-61aa8dd8a911&from=3&is-single=false&record_type=2

录制：面向复杂系统的人工智能 日期：2024-05-09 13:17:40 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=50a6a092-d5bc-414b-85e8-d626452ffcb2&from=3&is-single=false&record_type=2

录制：面向复杂系统的人工智能 日期：2024-05-16 13:13:52 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=de72ecac-8b10-4cf0-92fd-52aa5cd8af43&from=3&is-single=false&record_type=2

录制：面向复杂系统的人工智能 日期：2024-05-23 13:17:38 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=705c04f4-0dee-4e98-b1db-2f19b37ff778&from=3&is-single=false&record_type=2

录制：面向复杂系统的人工智能 日期：2024-05-30 13:14:40 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=9488f343-a152-4f6a-9bbf-6f1fe811b0c7&from=3&is-single=false&record_type=2

录制：面向复杂系统的人工智能 日期：2024-06-06 13:14:53 录制文件：https://meeting.tencent.com/v2/cloud-record/share?id=8a67b55e-ab3f-4c40-9cd3-68a8a62ec9e2&from=3&is-single=false&record_type=2

Outline

https://o6n8gxzxdg.feishu.cn/docx/SnetdbMUYo7m5uxELmZcrFKgnjf?from=from_copylink

Source

教材

• 集智俱乐部：深度学习原理与 PyTorch 实战（第 2 版），人民邮电出版社，2022
• Ian Goodfellow, Yoshua Bengio: Deep Learning, MIT, 2016（有中文版）
• Stuart Russell, Peter Norvig: Artificial Intelligence: A Modern Approach, Prentice Hall, 2010
• Sergios Theodoridis, Konstantinos Koutroumbas: Pattern Recognition, 2003
• George F. Luger, Artificial intelligence, Pearson Education Limited, 2002 5、朱迪亚·铂尔 （著）、刘礼等（译）：因果论，机械工业出版社，2022

参考课程

• Jure Leskovec: Machine Learning with Graphs, StanfordCS224W. https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn
• Steve Brunton: Data Driven Science and Engineering, University of Washington https://www.youtube.com/playlist?list=PLMrJAkhIeNNRpsRhXTMt8uJdIGz9-X_1-
• Karthik Duraisamy: DATA-DRIVEN ANALYSIS AND MODELING OF COMPLEX SYSTEMS, Michigen institute for computational discovery and engineering, Michigen University. https://micde.umich.edu/academic-programs-old/data-driven-course/
• Sergey Levine: Deep Reinforcement Learning, CS 285 at UC Berkeley. http://rail.eecs.berkeley.edu/deeprlcourse/
• 对复杂系统连续变化自动建模——Neural Ordinary Differential Equations解读 https://campus.swarma.org/course/2046
• 复杂网络自动建模在大气污染中的应用 https://campus.swarma.org/course/1998
• 两套因果框架深度剖析：潜在结果模型与结构因果模型 https://campus.swarma.org/course/2526
• 稳定学习：发掘因果推理和机器学习的共同基础 https://campus.swarma.org/course/2323
• 因果强化学习 https://campus.swarma.org/course/2156
• 张江：因果与机器学习能够破解涌现之谜吗 https://campus.swarma.org/course/4540
• 因果涌现理论提出者：Erik Hoel主题报告 https://campus.swarma.org/course/4317
• 如何从数据中发现因果涌现——神经信息压缩器 https://campus.swarma.org/course/4874
• 标准化流技术简介 https://campus.swarma.org/course/1999
• 带隐状态的强化学习世界模型 https://campus.swarma.org/course/4848

前置课程

• 机器学习
• 深度学习原理与PyTorch：https://campus.swarma.org/course/956

必读文献

• Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013 Aug;35(8):1798-828. doi: 10.1109/TPAMI.2013.50. PMID: 23787338.
• Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud: Neural Ordinary Differential Equations, Proceedings of the 32nd International Conference on Neural Information Processing Systems,12,6572–6583, NIPS 18