Understanding Bayesian Deep Learning

1. Elementary mathematics

• Set theory
• Measure theory
• Probability
• Random variable
• Random process
• Functional analysis (harmonic analysis)

2. Gaussian process

• 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