Undergraduate Course: Nonparametric Regression Models (MATH10104)
|School||School of Mathematics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 10 (Year 4 Undergraduate)
||Availability||Not available to visiting students
|Summary||A regression function is an important tool for describing the relation between two or more random variables. In real life problems, this function is usually unknown but it can be estimated from a sample of observations. Nonparametric methods are flexible techniques dedicated to treat general cases where the shape of the regression curve is unknown.
In this course we will introduce nonparametric regression models and how these can be applied in practice using R.
Topics to be covered include:
- Kernel smoothing;
- Reproducing Kernel Hilbert Spaces in machine learning including deep neural networks
- Use of R for fitting non-parametric models.
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- Knowledge of a range of methods for nonparametric regression and be able to apply them
- The ability to study asymptotic properties of nonparametric estimators
- The ability to use R to fit nonparametric regression models
|Graduate Attributes and Skills
|Course organiser||Dr Natalia Bochkina
Tel: 0131 650 8597
|Course secretary||Miss Greta Mazelyte