This course introduces methods to extract important patterns/information from data in fundamental science areas and presents basic concepts of machine learning from a statistical perspective. In particular, it emphasizes on the selection of appropriate methods and justification of choice, use of programming for implementation of the method, and evaluation and effective communication of results in data analysis reports. Topics covered include data preprocessing, visualization, statistical model selection/validation, matrix algebra, dimension reduction techniques, Bayesian decision theory, supervised and unsupervised learning problems, convex optimization, kernel methods, neural networks, information theory, etc.