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Classroom Contents
The Quest for Adaptivity in Machine Learning - Comparing Popular Methods
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- 1 Intro
- 2 Supervised machine learning Classical formalization
- 3 Local averaging
- 4 Curse of dimensionality on X = Rd
- 5 Support of inputs
- 6 Smoothness of the prediction function
- 7 Latent variables
- 8 Need for adaptivity
- 9 From kernels to neural networks
- 10 Regularized empirical risk minimization
- 11 Adaptivity of kernel methods
- 12 Adaptivity of neural networks
- 13 Comparison of kernel and neural network regimes
- 14 Optimization for neural networks
- 15 Simplicity bias
- 16 Overfitting with neural networks
- 17 Conclusion
