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Machine Learning Basics: A Speedrun - IPAM at UCLA
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- 1 Intro
- 2 Parameters
- 3 Inductive bias
- 4 Underfitting and overfitting
- 5 Considerations
- 6 Illustration
- 7 Optimal model complexity
- 8 Regularization terms
- 9 Crossvalidation
- 10 Data limitations
- 11 Linear regression
- 12 Ridge regression
- 13 Nonlinear regression
- 14 Kernel track
- 15 Kernel retrogression
- 16 Kernel as linear operator
- 17 Kernel trick
- 18 Energy contributions
- 19 Matrix factorization
- 20 Matrix iterative optimization
- 21 Preconditioning
- 22 Tradeoff
- 23 Nonlinearity
