Caltech关于机器学习的介绍性课程

zz:http://www.bfcat.com/index.php/2013/06/caltech-ml-course/#609096-tsina-1-25495-664964b7cb7b56e927fedca44cbe8c7d

这是加州理工大学的机器学习课程，里面包括了机器学习的理论，算法以及应用。约有来自15个不同专业的本科生和研究生学了这门课。现在有了网络资源，大家可以按照自己的进度学习。

http://work.caltech.edu/telecourse.html

• Lecture 1: The Learning Problem
• Lecture 2: Is Learning Feasible?
• Lecture 3: The Linear Model I
• Lecture 4: Error and Noise
• Lecture 5: Training versus Testing
• Lecture 6: Theory of Generalization
• Lecture 7: The VC Dimension
• Lecture 8: Bias-Variance Tradeoff
• Lecture 9: The Linear Model II
• Lecture 10: Neural Networks
• Lecture 11: Overfitting
• Lecture 12: Regularization
• Lecture 13: Validation
• Lecture 14: Support Vector Machines
• Lecture 15: Kernel Methods
• Lecture 16: Radial Basis Functions
• Lecture 17: Three Learning Principles
• Lecture 18: Epilogue