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Bayesian Deep Learning

최성준

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Bayesian Deep Learning

Bayesian Deep Learning

http://www.boostcourse.org/bayesiandeeplearning

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강의목록
1. Introduction
2. Set theory
3. Measure theory
4. Probability
5. Random variable
6. Random process
7. Functional analysis
1. Introduction
2. Gaussian process
3. Weight space view
4. Function space view
5. Gaussian process latent variable model (GPLVM)
6. Gaussian process Application : 최성준님 연구 소개
1. Introduction
2. Minimizing the Description Length
3. Ensemble Learning in Bayesian Neural Network
4. Practical variational inference
5. Bayes by backprop (BBB)
1. Summary of Variational Inference
2. Dropout as a Bayesian Approximation
3. Stein Variational Gradient Descent
1. Summary
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강의목록 Help

CHAPTER 1. Elementary of mathematics

강의시간02:06:58
CHAPTER 2. Gaussian process

강의시간01:39:48
CHAPTER 3. Bayesian deep neural network (1)

강의시간01:16:22
CHAPTER 4. Bayesian deep neural network (2)

강의시간01:28:13
CHAPTER 5. Summary

강의시간45:53
- Summary
  
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  2023.06.01
  
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  강의시간45:53
CHAPTER 6. Uncertainties in Deep Learning

강의시간01:34:26

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