Postgraduate Programme and Module Handbook 2023-2024 (archived)
Module MATH52015: Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning
Department: Mathematical Sciences
MATH52015:
Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning
Type |
Tied |
Level |
5 |
Credits |
15 |
Availability |
Available in 2023/24 |
Module Cap |
None. |
Prerequisites
- PHYS51915 Core Ia: Introduction to Machine Learning and Statistics AND PHYS52015 Core Ib: Introduction to Scientific and High-Performance Computing
Corequisites
Excluded Combination of Modules
Aims
- Provide advanced knowledge and critical understanding of the paradigms and
fundamental ideas of Bayesian statistics and machine learning.
- Provide advanced knowledge and critical understanding of the methodology and applications of Bayesian statistics and machine learning.
Content
- Bayesian theory, inference, and computation (e.g. foundations, probability and decision theory, sampling methods, variational methods).
- Unsupervised learning (e.g. density estimation, kernels, clustering, EM, etc.)
Learning Outcomes
- Advanced understanding of Bayesian theory, inference, and computationally-intentensive methods and algorithms.
- Advanced understanding of unsupervised machine learning frameworks and methods.
- Ability to use Bayesian theory and inference to frame, analyse, and formalize practical problems, and to reflect critically upon this use.
- Ability to select and apply appropriate computationally-intensive methods to practical problems, and to reflect critically upon their application.
- Ability to select or to develop, and to apply, appropriate models to practical problems, and to reflect critically upon their application.
- Ability to select, adapt, and apply appropriate machine learning methods to practical problems, and to reflect critically upon their application.
Modes of Teaching, Learning and Assessment and how these contribute to
the learning outcomes of the module
- Lectures demonstrate what is required to be learned and the application of the theory to concrete examples.
- Practical classes concretize understanding via the application of calculational and computational methods to more complex problems, as well as providing feedback and encouraging active engagement.
- Coursework will assess students' ability to implement calculational and computational methods on both synthetic and real problems.
Teaching Methods and Learning Hours
Activity |
Number |
Frequency |
Duration |
Total/Hours |
|
Lectures on Foundations |
12 |
3 per week, weeks 11 - 14, term 2 |
1 hour |
12 |
|
Practical classes on Foundations |
4 |
1 per week, weeks 11 - 14, term 2 |
1 hour |
4 |
|
Lectures on Unsupervised Learning |
12 |
3 per week, weeks 11 - 14, term 2 |
1 hour |
12 |
|
Practical classes on Unsupervised Learning |
4 |
1 per week, weeks 11 - 14, term 2 |
1 hour |
4 |
|
Preparation, reading, and self-study |
|
|
|
118 |
|
Summative Assessment
Component: Coursework |
Component Weighting: 100% |
Element |
Length / duration |
Element Weighting |
Resit Opportunity |
Coursework on Foundations |
5 weeks |
50% |
|
Coursework on Unsupervised Learning |
5 weeks |
50% |
|
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University