Marcel Dettling: Catalogue data in Spring Semester 2018

Name	Dr. Marcel Dettling
Address	ZHAW - IDP 8401 Winterthur SWITZERLAND
Telephone	058 934 70 23
E-mail	marcel.dettling@math.ethz.ch
Department	Mathematics
Relationship	Lecturer

Number	Title	ECTS	Hours	Lecturers
401-0622-00L	Mathematical Foundations II: Linear Algebra and Statistics	3 credits	2V + 1U	M. Dettling
Abstract	Systems of linear equations; matrix algebra, determinants; vector spaces, norms and scalar products; linear maps, basis transformations; eigenvalues and eigenvectors. Random variables and probability, discrete and continuous distribution models; expectation, variance, central limit theorem, parameter estimation; statistical hypothesis tests; confidence intervals; regression analysis.
Learning objective	A sound knowledge of mathematics is an essential prerequisite for a quantitative and computer-based approach to natural sciences. In an intensive two-semester course the most important basic concepts of mathematics, namely univariate and multivariate calculus, linear algebra and statistics are taught.
Content	Systems of linear equations; matrix algebra, determinants; vector spaces, norms and scalar products; linear maps, basis transformations; eigenvalues and eigenvectors. - Least squares fitting and regression models; random variables, statistical properties of least-squares estimators; tests, confidence and prediction intervals in regression models; residual analysis.
Lecture notes	For the part on Linear Algebra, there is a short script (in German) which summarizes the main concepts and results without examples. For a self-contained presentation, the book by Nipp and Stoffer should be used. For the part on Statistics there is a detailed script (in German) available which should be self-contained. The book by Stahel can be used for additional information.
Literature	Linear Algebra: K. Nipp/D. Stoffer: "Lineare Algebra", vdf, 5th edition. Statistics: W. Stahel, "Statistische Datenanalyse", Vieweg, 3rd edition.
401-6624-11L	Applied Time Series	5 credits	2V + 1U	M. Dettling
Abstract	The course starts with an introduction to time series analysis (examples, goal, mathematical notation). In the following, descriptive techniques, modeling and prediction as well as advanced topics will be covered.
Learning objective	Getting to know the mathematical properties of time series, as well as the requirements, descriptive techniques, models, advanced methods and software that are necessary such that the student can independently run an applied time series analysis.
Content	The course starts with an introduction to time series analysis that comprises of examples and goals. We continue with notation and descriptive analysis of time series. A major part of the course will be dedicated to modeling and forecasting of time series using the flexible class of ARMA models. More advanced topics that will be covered in the following are time series regression, state space models and spectral analysis.
Lecture notes	A script will be available.
Prerequisites / Notice	The course starts with an introduction to time series analysis that comprises of examples and goals. We continue with notation and descriptive analysis of time series. A major part of the course will be dedicated to modeling and forecasting of time series using the flexible class of ARMA models. More advanced topics that will be covered in the following are time series regression, state space models and spectral analysis.
447-6624-01L	Applied Time Series I Only for DAS and CAS in Applied Statistics.	2 credits	1V + 1U	M. Dettling
Abstract	Introduction to time series analysis: examples, goals and mathematical notation. Descriptive techniques, modelling and prediction.
Learning objective	Getting to know the mathematical properties of time series, as well as the requirements, descriptive techniques, models and software that are necessary such that the student can independently run an applied time series analysis.
Content	The course starts with an introduction to time series analysis that comprises of examples and goals. We continue with notation and descriptive analysis of time series. A major part of the course will be dedicated to modeling and forecasting of time series using the flexible class of ARMA models.
Lecture notes	A script will be available.
447-6624-02L	Applied Time Series II Only for DAS and CAS in Applied Statistics.	4 credits	1V + 1U	M. Dettling
Abstract	More advanced topics in time series analysis like time series regression, state space models and spectral analysis.
Learning objective	Getting to know advanced methods and software that are necessary such that the student can independently run an applied time series analysis.
Lecture notes	A script will be available.