LearningFromData

Practice the algorithm from the edx class Learning From Data

###Schedule

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

###Currently Finished

1. Perceptron