THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2023/2024

Timetable information in the Course Catalogue may be subject to change.

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: MIGS: Computational Methods for Data Driven Modelling (MATH11220)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits15 ECTS Credits7.5
SummaryTopics covered will include-

- Optimisation
- Bayesian inference
- Sampling methods
- Uncertainty qualification

The course uses an intuitive hands-on approach with Python as a platform and will include case studies from industrial partners.
Course description Mainly in the first two semesters, opportunities to attend generic skills training will be made available to you and you are encouraged to make the most of this. It may also be possible to arrange specific training to meet a demand if several students are interested in a specific area.

Semester 1
The SMSTC Symposium in Perth includes workshops on tutoring, marking and how to get a PhD.
Computer Tools & Skills I & II micro project report, and write summary of talk in MAC-MIGS Colloquium.
Presentation skills

Semester 2
Maths Modelling Camp.
Short presentation at MAC-MIGS Residential Symposium.

All Year
Gain experience of writing LaTeX in SMSTC assignments. Store in Training Log.

We expect students to attend general mathematical activities such as seminars, discussions, colloquia and EMS meetings where possible, provided they do not clash with taught courses and other 1st Year MAC-MIGS programme activities.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements Students wishing to enrol on this course must contact pgresearch@maths.ed.ac.uk for further information.
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  13
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 150 ( Lecture Hours 20, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 127 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% coursework
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand the differences and the similarities between traditional deterministic regularization methods and their Bayesian counterparts for inverse problems.
  2. Be able to solve computationally (non smooth and smooth) convex optimisation problems using Python
  3. Understand basic concepts about Bayesian inference
  4. Be able to sample from high dimensional probability distributions using Python
  5. Familiarise themselves with machine learning approaches for solving inverse problems
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
Special Arrangements This course is only open to students on CDT programmes in the Maxwell Institute Graduate School.
KeywordsMIGS;CDT
Contacts
Course organiserDr Benjamin Goddard
Tel: (0131 6)50 5127
Email: B.Goddard@ed.ac.uk
Course secretaryMrs Katy Cameron
Tel:
Email: Katy.Cameron@ed.ac.uk
Navigation
Help & Information
Home
Introduction
Glossary
Search DPTs and Courses
Regulations
Regulations
Degree Programmes
Introduction
Browse DPTs
Courses
Introduction
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Prospectuses
Important Information