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 4 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.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Statistical Methodology (MATH10095)
|
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
|
| Academic year 2018/19, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
69 )
|
| Assessment (Further Info) |
Written Exam
95 %,
Coursework
5 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 5%; Examination 95% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | MATH10094 Statistical Learning | 2:00 | |
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.
|
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 | Dr Gordon Ross
Tel: (0131 6)50 51111
Email: Gordon.Ross@ed.ac.uk |
Course secretary | Mrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email: Alison.Fairgrieve@ed.ac.uk |
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