The course will cover an introductory level of Computational Statistics and Machine Learning methods. The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial simulation methods, sampling methods, bootstrap, and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large datasets in practice. Finally, this unit will show how to make inferences about populations of interest in data mining problems. This course will analyze the data mainly using R. We will not pursue the mathematical details but require students to be able to interpretate their data analysis results. After learning the course, students are expected to be equipped with necessary statistical knowledge for real data analysis and to be proficient in using R for regression, classification, prediction, etc.