Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. This course will cover the fundamental principles and usage methods of classic machine learning algorithms.

  • Time: Tuesday 9:50-11:25, Thursday 14:00-15:35, Thursday 18:30-20:55 (Week 4-10, 双周), 18:30-20:05 (Week 12-14, 双周)
  • Location: 教二-405, Jiulonghu Campus
  • Instructor: Associate Researcher Tong Wei (weit@seu.edu.cn)
  • Teaching Assistants: Yi-Bo Wang (wang_yb@seu.edu.cn), Bo-Lin Wang (wangbl@seu.edu.cn), Zeng Han (zeng_han@seu.edu.cn)
  • Grading: Final exam (60%) + Assignments (40%)
  • Discussion: QQ (699720665)

作业提交要求 （非常重要）

  • 作业上传地址：东大云盘（注意：请将作业上传到相应的目录）
  • 作业通过 zip 压缩包上传，压缩包和解压后文件夹名称均为：学号_姓名_1 （若重复提交，请修改后缀 2,3,…，助教将依据最大编号版本判分）
  • 理论课作业、实验课报告请务必使用 PDF 格式，不接受 WORD、图片等其它文件类型
  • 未按时提交的作业将自动扣除一定比例分数 (24h内最高得50%分数，超出24h得0分)
  • 作业抄袭按 0 分计算

Textbooks

  • Pattern Recognition and Machine Learning, Chris Bishop. (Strongly recommended)
  • Machine Learning: a Probabilistic Perspective, Kevin Murphy. (Optional)
  • Foundations of Machine Learning, Mehryar Mohri. (Optional)
  • Machine Learning, Tom Mitchell. (Optional)
  • Additional readings will be made available as appropriate.