Monte Carlo methods 1 Introduction to Monte Carlo Methods 2 Sequential Monte Carlo 3 Markov Chain Monte Carlo the Basics 4 Metropolis Methods and Variants 5 Gibbs Sampler and its Variants 6 Cluster Sampling Methods 7 Convergence Analysis of MCMC 8 Data Driven Markov Chain Monte Carlo 9 Hamiltonian and Langevin Monte Carlo 10 Learning with Stochastic Gradient 11 Mapping the Energy Landscape. This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods including Sequential Monte Carlo Markov Chain Monte Carlo Metropolis Hastings Gibbs Sampler Cluster Sampling Data Driven MCMC Stochastic Gradient descent Langevin Monte Carlo Hamiltonian Monte Carlo and energy landscape mapping. Due to its comprehensive nature the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision Computer Graphics Machine Learning Robotics Artificial Intelligence etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.