Undergraduate Course: Statistical Learning (MATH10094)
|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
|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.
Likely topics include:-
- supervised and unsupervised learning
- discriminant analysis
- support vector machines
- deep learning
- and-random forests
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||Dr Gordon Ross
Tel: (0131 6)50 51111
|Course secretary||Miss Sarah McDonald
Tel: (0131 6)50 5043