This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Postgraduate Handbook 2022-2023 (archived)
Module MATH52115: Advanced Statistics and Machine Learning: Regression and Classification
Department: Mathematical Sciences
MATH52115: Advanced Statistics and Machine Learning: Regression and Classification
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Tied | 5 | 15 | Available in 2022/2023 | None. | Durham |
Tied To
Pre-requisites
- MATH52015 Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning
Corequisites
- None
Excluded Combinations
- None
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
- Advanced statistical modelling (e.g. linear models, graphical models, Gaussian processes).
- Advanced supervised machine learning (e.g. random forests, boosting, deep neural networks, deep statistical models).
Learning Outcomes
Subject-specific Knowledge
- Advanced understanding of statistical modelling frameworks and methods.
- Advanced understanding of supervised machine learning frameworks and methods.
Subject-specific Skills
- 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.
Key Skills
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 | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures on Regression | 12 | 3 per week, weeks 16 - 19, term 2 | 1 hour | 12 | |
| Practical classes on Regression | 4 | 1 per week, weeks 16 - 19, term 2 | 1 hour | 4 | |
| Lectures on Classification | 12 | 3 per week, weeks 16 - 19, term 2 | 1 hour | 12 | |
| Practical classes on Classification | 4 | 1 per week, weeks 16 - 19, term | 1 hour | 4 | |
| Preparation, reading, and self-study | 118 | ||||
| Total | 150 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Coursework | 100% | Coursework on Regression | 5 weeks | 50% | |
| Coursework on Classification | 5 weeks | 50% |
Formative Assessment
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.