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 give an introduction to modern machine learning, from a statistical perspective. |
| Course description |
Likely topics include:-
- supervised and unsupervised learning
- classification
- regression
- discriminant analysis
- regularisation
- support vector machines
- deep learning
- and-random forests
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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 | Dr Gordon Ross
Tel: (0131 6)50 51111
Email: |
Course secretary | Miss Sarah McDonald
Tel: (0131 6)50 5043
Email: |
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