The lectures are about 60 minutes each plus Q&A. You can also look for a particular topic in the Machine Learning Video Library.

- 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

theory; mathematical

technique; practical

analysis; conceptual

The story linefrom Lecture 1 to Lecture 18 is:

- What is learning?
- Can we learn?
- How to do it?
- How to do it well?
- Take-home lessons.

via work.caltech.edu

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