1. Introduction
2. Background
2.1. Probabilistic Graphical Model and Markov Random Field
2.2. Gibbs Sampling
2.3. Statistical Physics and Ising Model
2.4. Hopfield Network
3. Restricted Boltzmann Machine
3.1. Structure of Restricted Boltzmann Machine
3.2. Conditional Distributions
3.3. Sampling Hidden and Visible Variables
3.4. Training Restricted Boltzmann Machine by Maximum Likelihood Estimation
3.5. Contrastive Divergence
3.6. Boltzmann Machine
4. Distributions of Visible and Hidden Variables
4.1. Modeling with Exponential Family Distributions
4.2. Binary States
4.3. Continuous Values
4.4. Discrete Poisson States
5. Conditional Restricted Boltzmann Machine
6. Deep Belief Network
6.1. Stacking RBM Models
6.2. Other Improvements over RBM and DBN
7. Conclusion