Learning From Data by Professor Yaser Abu-Mostafa.
Rather than being broken into smaller segments, these lectures are traditional lecture length.
Personally I prefer the longer lecture style over shorter snippets, such as were used for Learning from Data (an earlier version).
Lectures:
- 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)
Enjoy!