Chapter 1. Basics -- Chapter 2. Topic Models -- 3. Chapter 3. Pre-processing of Training Data -- Chapter 4. Expectation Maximization -- Chapter 5. Markov Chain Monte Carlo Sampling -- Chapter 6. Variational Inference -- Chapter 7. Distributed Training -- Chapter 8. Parameter Setting -- Chapter 9. Topic Deduplication and Model Compression -- Chapter 10. Applications.