401-3632-00L Computational Statistics
| Semester | Spring Semester 2015 |
| 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. |
| Learning 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 http://stat.ethz.ch/education/ . 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 http://stat.ethz.ch/education/ (-> "Computational Statistics"). |
| Lecture notes | lecture notes are available online; see http://stat.ethz.ch/education/ (-> "Computational Statistics"). |
| Literature | (see the link above, and the lecture notes) |
| Prerequisites / Notice | Basic "applied" mathematical calculus and linear algebra. At least one semester of (basic) probability and statistics. |