Postgraduate Course: MIGS: Computational Methods for Data Driven Modelling (MATH11220)
|School||School of Mathematics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||Topics covered will include-
- 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.
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.
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.
Maths Modelling Camp.
Short presentation at MAC-MIGS Residential Symposium.
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)
||Other requirements|| Students wishing to enrol on this course must contact firstname.lastname@example.org for further information.
Course Delivery Information
|Academic year 2023/24, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
|No Exam Information
On completion of this course, the student will be able to:
- Understand the differences and the similarities between traditional deterministic regularization methods and their Bayesian counterparts for inverse problems.
- Be able to solve computationally (non smooth and smooth) convex optimisation problems using Python
- Understand basic concepts about Bayesian inference
- Be able to sample from high dimensional probability distributions using Python
- Familiarise themselves with machine learning approaches for solving inverse problems
|Graduate Attributes and Skills
||This course is only open to students on CDT programmes in the Maxwell Institute Graduate School.
|Course organiser||Dr Benjamin Goddard
Tel: (0131 6)50 5127
|Course secretary||Mrs Katy Cameron