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Lecture notes on Bayesian deep learning
bayes-nn's Introduction
Understanding Bayesian Deep Learning
1. Elementary mathematics
- Set theory
- Measure theory
- Probability
- Random variable
- Random process
- Functional analysis (harmonic analysis)
- Gaussian process
- Weight space view
- Function space view
- Gaussian process latent variable model
3. Bayesian neural netwrok
- Minimum description length
- Ensemble learning in Bayesian neural network
- Practical variational inference
- Bayes by backprop
- Summary of variational inference
- Dropout as a Bayesian approximation
- Stein variational gradient descent
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