DL2026Fall - 2026年秋季学期《深度学习》（本科生 & 研究生）

Class Time and Location 上课时间和地点

• 本科生：周三 3-4节（上午10:30-12:15） 中国海洋大学西海岸校区综合体南楼506/505
• 研究生：周三 10-12节（晚上6:30-9:20） 中国海洋大学西海岸校区信息北楼A419

Instructors 教学团队

• 主讲教师：郑海永 (Haiyong Zheng)
• 助教团队：李思为（Siwei Li） 周闯闯（Chuangchuang Zhou）

TextBook 参考书

• Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press, 2016.
• Computer Vision: Algorithms and Applications, by Richard Szeliski, Springer, 2010.
• Digital Image Processing (3rd edition), by Rafael Gonzalez and Richard Woods, Prentice Hall, 2008.
• Mathematics for Machine Learning, by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Cambridge University Press, 2020.
• Neural Networks and Deep Learning, by Michael Nielsen, 2019.
• 《机器学习》，周志华，清华大学出版社，2016年。
• Dive into Deep Learning, an interactive deep learning book with code, math, and discussions.
• Awesome Computer Vision: a curated list of awesome computer vision resources.

Grading Policy 成绩构成

总成绩 ${G}_{total}={G}_{class} + {G}_{assignment} + {G}_{project} + {G}_{award} - {G}_{penalty} - {G}_{absence}$

• 课堂表现成绩 $G_{class}$：课堂编程练习（In-Class Programming Practice）【个人表现】
• 作业表现成绩 $G_{assignment}$：课外编程写作（Outside-Class Programming Practice）【个人表现】
• 项目表现成绩 $G_{project}$：报告演示表现（Report and Presentation）【1-2人/组】
• 优异表现加分 $G_{award}$：课内外优异表现
• 不良表现扣分 $G_{penalty}$：课内外不良表现
• 缺勤扣分 $G_{absence}$：
  • 无故 缺勤一次：${G}_{absence}=10$
  • 无故 缺勤二次：${G}_{absence}=30$
  • 无故 缺勤三次：${G}_{absence}=60$

TOP 返回顶部

Syllabus and Lecture Notes 教学大纲和课堂讲义

Assignment/Project Submission:

• 海码平台
• 超星泛雅

TOP 返回顶部

References 参考课程

• The Deep Learning Specialization by Andrew Ng.
• EECS498: Deep Learning for Computer Vision (Winter 2022) by Justin Johnson.
• CS231n: Deep Learning for Computer Vision (Spring 2022) by Fei-Fei Li.
• NYU Deep Learning by Yann LeCun and Alfredo Canziani.
• STAT157: Introduction to Deep Learning by Alex Smola and Mu Li.
• MIT6.S191: Introduction to Deep Learning by Alexander Amini and Ava Soleimany.
• MIT Deep Learning and Artificial Intelligence Lectures by Lex Fridman and others.
• Deep Learning Introduction by Liang Wang.
• Deep Learning Theory and Deep Learning for Human Language Processing by Hongyi Li.
• CAP6412: Advanced Computer Vision by Mubarak Shah.
• COMSM0045: Applied Deep Learning by Dima Damen and Tilo Burghardt.
• CMU10707: Deep Learning by Ruslan Salakhutdinov.
• Deep Learning with PyTorch: Zero to GANs by Aakash N S.

TOP 返回顶部

Sources 相关资源

• Teaching at Center for Research in Computer Vision of University of Central Florida, by Mubarak Shah.
• Teaching at Computer Graphics, Vision, Animation, and Game Science of University of Washington, by Steven Seitz.
• Teaching at Vision Lab of Stanford University, directed by Fei-Fei Li.
• Teaching at UT-Austin Computer Vision Group of University of Texas at Austin, directed by Kristen Grauman.
• Teaching at Computer Science and Artificial Intelligence Laboratory of Massachusetts Institute of Technology, by Antonio Torralba.
• Teaching at Graphics, Visualization, and Interaction Group of Brown University, by James Hays.
• Teaching at Computational Vision & Geometry Lab of Stanford University, directed by Silvio Savarese.
• Teaching at Serre Lab of Brown University, directed by Thomas Serre.
• Teaching at Vision & Learning Lab of University of Wisconsin Milwaukee, directed by Ali Borji.
• Leading Journals and Conferences in Computer Vision
  • International Journal of Computer Vision (IJCV)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE/CVF International Conference on Computer Vision (ICCV)
  • European Conference on Computer Vision (ECCV)

TOP 返回顶部

Tools 工具

• PyTorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration
• TensorFlow: An open-source software library for Machine Intelligence
• Caffe: Deep Learning Framework
• MXNet: Flexible and Efficient Library for Deep Learning
• OpenCV: Open Source Computer Vision
• VLFeat: Vision Lab Features Library
• scikit-image: Image Processing in Python
• scikit-learn: Machine Learning in Python

TOP 返回顶部

Contact 联系方式

• 郑海永 zhenghaiyong@ouc.edu.cn

TOP 返回顶部

---

Updated: Sep. 23, 2026	Home / Index	Edited by Haiyong Zheng