The aim of this course is to provide students with an understanding of design of experiments and regression methods, to develop their abilities to design and analyse physical and computer experiments, and to impress on them the value of such systematic approaches. Experimental designs for physical and computer experiments such as orthogonal arrays and space-filling designs will be introduced, and students will learn how and when to use these designs. The course will develop students’ grasp of fundamental regression techniques for analysing data from physical experiments, which include linear models, least squares method, analysis of variance, and model selection approaches, and their ability to apply these techniques. In addition, students will learn to apply Gaussian process models for approximating highly nonlinear functional relationships from computer experiments.