Undergraduate Course: Statistical Learning (MATH10094)
Course Outline
| 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 |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| 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. |
| Course description |
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.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Statistical Methodology (MATH10095)
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Co-requisites | |
| Prohibited Combinations | |
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? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
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.
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Reading List
| The Elements of Statistical Learning. Hastie, Tibshirani and Friedman. Springer. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | SLe,Statistics |
Contacts
| Course organiser | Prof Ruth King
Tel: (0131 6)50 5947
Email: Ruth.King@ed.ac.uk |
Course secretary | Mrs Noureen Ehsan
Tel: (0131 6)51 1532
Email: Noureen.Ehsan@ed.ac.uk |
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