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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Nonparametric Regression Models (MATH10104)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryA 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.
Course description Topics to be covered include:

- Kernel smoothing;
- Splines;
- Wavelets;
- Reproducing Kernel Hilbert Spaces in machine learning including deep neural networks
- Use of R for fitting non-parametric models.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Fundamentals of Pure Mathematics (MATH08064) AND Probability (MATH08066) AND Statistics (Year 2) (MATH08051) AND Several Variable Calculus and Differential Equations (MATH08063)
Co-requisites
Prohibited Combinations Other requirements Students are recommended to have taken Metric Spaces (MATH10101)
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. Knowledge of a range of methods for nonparametric regression and be able to apply them
  2. The ability to study asymptotic properties of nonparametric estimators
  3. The ability to use R to fit nonparametric regression models
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNonparametric,Statistics,NRM
Contacts
Course organiserDr Natalia Bochkina
Tel: 0131 650 8597
Email: n.bochkina@ed.ac.uk
Course secretaryMiss Greta Mazelyte
Tel:
Email: greta.mazelyte@ed.ac.uk
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