Undergraduate Course: Statistical Learning (MATH10094)
|School||School of Mathematics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 10 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||NB. This course is delivered *biennially* with the next instance being in 2018-19. It is anticipated that it would then be delivered every other session thereafter.
This course will introduce the ideas behind statistical learning, using both supervised and unsupervised techniques, and exploring classification techniques.
The topics will include a selection from the following areas :
- linear shrinkage methods;
- model assessment and selection;
- tree-based methods;
- neural networks;
- random forests; and
- graphical models.
Entry Requirements (not applicable to Visiting Students)
|| Students MUST have passed:
Statistical Methodology (MATH10095)
||Other requirements|| None
Information for Visiting Students
|Pre-requisites||Visiting students are advised to check that they have studied the material covered in the syllabus of any pre-requisite course listed above before enrolling.
|High Demand Course?
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- understand the different types of learning algorithms : supervised and unsupervised.
- understand different data mining approaches.
- apply different statistical techniques to data and interpret the results accordingly.
- apply different techniques using R.
|The Elements of Statistical Learning. Hastie, Tibshirani and Friedman. Springer.|
|Graduate Attributes and Skills
|Course organiser||Prof Ruth King
Tel: (0131 6)50 5947
|Course secretary||Mrs Noureen Ehsan
Tel: (0131 6)51 1532