Syllabus
- Part I. Basics
- Nearest neighbor prediction and density estimation
- Kernel prediction and density estimation
- Non-parametric density-based clustering
- Crossvalidation
- The Boostrap
- Part II. Intermediate
- Double descent
- Kernel Machines
- [Random Forests]
- Boosting
- [Manifold learning]
- Part III. Advanced
- Gaussian processes (GPs)
- Neural networks as GPs and the Neuro-Tangent kernel
- Random Fourier Features (RFF)
- NP-Bayes, Dirichlet processes for non-parametric clustering
- [shape constrained estimation, computational issues with big data, conformal prediction]
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Some advanced topics and topics in [] may be skipped, depending on time constraints and interest.
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