Programming assignments from Cal Tech's Machine Learning class by Professor Yaser Abu-Mostafa.
Here is great site by blaenk with notes summarizing the entire class.
The outline of the class:
- 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