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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2019/2020

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Statistical Learning (MATH10094)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryNB. 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
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-requisitesVisiting 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:
  1. Understand the different types of learning algorithms : supervised and unsupervised.
  2. Understand different data mining approaches.
  3. Apply different statistical techniques to data and interpret the results accordingly.
  4. 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
KeywordsSLe,Statistics
Contacts
Course organiserDr Gordon Ross
Tel: (0131 6)50 51111
Email: Gordon.Ross@ed.ac.uk
Course secretaryMiss Sarah McDonald
Tel: (0131 6)50 5043
Email: sarah.a.mcdonald@ed.ac.uk
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