# 401-3632-00L  Computational Statistics

 Semester Spring Semester 2017 Lecturers M. Mächler, P. L. Bühlmann Periodicity yearly recurring course Language of instruction English

 Abstract "Computational Statistics" deals with modern methods of data analysis (aka "data science") for prediction and inference. An overview of existing methodology is provided and also by the exercises, the student is taught to choose among possible models and about their algorithms and to validate them using graphical methods and simulation based approaches. Objective Getting to know modern methods of data analysis for prediction and inference.Learn to choose among possible models and about their algorithms. Validate them using graphical methods and simulation based approaches. Content Course Synopsis:multiple regression, nonparametric methods for regression and classification (kernel estimates, smoothing splines, regression and classification trees, additive models, projection pursuit, neural nets, ridging and the lasso, boosting). Problems of interpretation, reliable prediction and the curse of dimensionality are dealt with using resampling, bootstrap and cross validation.Details are available via https://stat.ethz.ch/lectures/ .Exercises will be based on the open-source statistics software R (http://www.R-project.org/). Emphasis will be put on applied problems. Active participation in the exercises is strongly recommended.More details are available via the webpage https://stat.ethz.ch/lectures/ (-> "Computational Statistics"). Lecture notes lecture notes are available online; seehttp://stat.ethz.ch/lectures/ (-> "Computational Statistics"). Literature (see the link above, and the lecture notes) Prerequisites / Notice Basic "applied" mathematical calculus (incl. simple two-dimensional) and linear algebra (including Eigenvalue decomposition) similar to two semester "Analysis" in an ETH (math or) engineer's bachelor.At least one semester of (basic) probability and statistics, as e.g., taught in an ETH engineer's or math bachelor.Programming experience in either a compiler-based computer language (such as C++) or a high-level language such as python, R, julia, or matlab. The language used in the exercises and the final exam will be R (https://www.r-project.org) exclusively. If you don't know it already, some extra effort will be required for the exercises.