Chapter 1, *Introduction to Markov Process*, starts with a discussion of basic probability theory, and then introduces Markov chains. The chapter also talks about the different types of Markov chain classifying based on continuous or discrete states and time intervals.

Chapter 2, *Hidden Markov Models*, builds on the concept of Markov processes and DBNs to introduce the concepts of the HMM.

Chapter 3, *State Inference – Predicting the States*, introduces algorithms that can be used to predict the states of a defined HMM. The chapter introduces the Forward algorithm, the backward algorithm, the forward-backward algorithm, and the Viterbi algorithm.

Chapter 4, *Parameter Inference Using Maximum Likelihood*, discusses the basics of maximum likelihood learning. The chapter then moves on to applying maximum likelihood learning in the case of HMMs and introduces the Viterbi learning algorithm and Baum-Welch algorithm.

Chapter 5, *Parameter Inference Using Bayesian Approach*, starts by introducing the basic concepts of Bayesian learning. The chapter then applies these concepts in the case of HMMs and talks about the different approximation methods used for learning using the Bayesian method.

Chapter 6, *Time Series Predicting*, discusses the application of HMMs in the case of time series data. The chapter takes the example of the variation of stock prices and tries to model it using an HMM.

Chapter 7, *Natural Language Processing*, discusses the application of HMMs in the field of speech recognition. The chapter discusses two main areas of application: part-of-speech tagging and speech recognition.

Chapter 8, *2D HMM for Image Processing*, introduces the concept of 2D HMMs and discusses their application in the field of image processing.

Chapter 9, *Markov Decision Process*, introduces the basic concepts of reinforcement learning and then talks about Markov decision process and introduces the Bellman equation to solve them.