Learning From Data

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!

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