Chapter 1 前言

本系列讲义为 Jim 根据自己的理解，对于清华大学自动化系教授、ISCB Fellow、生命学院和医学院兼职教授、R 语言DEGseq库作者张学工老师于 2022 年秋季开设的《Machine Learning》课程讲义进行的重新诠释。

1.1 课程大纲

Week Date Course content
1 09/15 Introdution, Pattern Classifiers and Their Assessment
2 09/22 Linear Learning Machines (Linear Classifiers, Linear Regression, MSE/ADALINE, Logistic Regression, Fisher’s Linear Discriminant, Perceptron)

1.2 课程参考书

  1. 张学工、汪小我，《模式识别（第四版）：模式识别与机器学习》，清华大学出版社，2021.8.
  2. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification (2nd edition), John Wiley & Sons, Inc, 2001.
  3. S. Raschka & V. Mirjalili, Python Machine Learning (2nd edition), Birmingham, Packt Publishing, 2017.
  4. Y. S. Abu-Mostafa, M. Magdon-Ismail, H-T. Lin, Learning from Data, AMLbook.com, 2012.
  5. Christopher M. Bishop, Pattern Recognition and Machine Learning, New York, Springer, 2006.
  6. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning (2nd edition), New York, Springer, 2016.
  7. S. Shalev-Shwartz & S. Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
  8. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016.