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DSC3007 : Statistical Inference and Learning
- Inactive for Year: 2026/27
- Module Leader(s): Dr Pete Philipson
- Lecturer: Dr Markus Rau
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module develops your understanding of advanced approaches to statistical inference, modelling, and learning from data. You will study methods for constructing, evaluating, and refining statistical and machine learning models, including approaches for high-dimensional, non-linear, and complex data settings. The emphasis is on reasoning under uncertainty, understanding the assumptions and limitations of different modelling approaches, and critically interpreting results within realistic and data-intensive applications.
Outline Of Syllabus
Topics covered by this module include:
Advanced statistical inference and learning from data
Generalised linear models and non-linear modelling approaches
Splines, smoothing methods, and flexible regression techniques
Kernel-based methods and high-dimensional modelling approaches
Regularisation and model complexity control
Functional data analysis and structured data representations
Deep learning methods and neural network-based modelling
Reinforcement learning and adaptive modelling approaches
Missing data, incomplete observations, and imputation techniques
Model evaluation, uncertainty quantification, and interpretation of results
Applications of advanced statistical and machine learning methods to complex real-world datasets
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 31 | 1:00 | 31:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Preparation and completion of summative assessment |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Preparation and completion of formative assessment |
| Structured Guided Learning | Lecture materials | 40 | 1:00 | 40:00 | Reading through lecture materials – preparing / follow up |
| Guided Independent Study | Assessment preparation and completion | 1 | 57:00 | 57:00 | Independent study and revision for final written exam |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:00 | 2:00 | Completion of written exam |
| Guided Independent Study | Directed research and reading | 20 | 1:00 | 20:00 | Wider reading as per provided reading list |
| Scheduled Learning And Teaching Activities | Small group teaching | 10 | 1:00 | 10:00 | Group tutorials (or practical/lab) |
| Total | 200:00 |
Teaching Rationale And Relationship
This module includes an encounter with the leading edge of research and practice.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 120 | 1 | A | 80 | N/A |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Computer assessment | 1 | M | 20 | Interactive in-class test analysing a data set, of 50 mins |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Computer assessment | 1 | M | 50 mins NUMBAS assessment or equivalent |
Assessment Rationale And Relationship
The written examination assesses students’ understanding and application of advanced statistical inference, modelling, and learning approaches within controlled conditions. The examination evaluates students’ ability to reason about model construction, assumptions, uncertainty, regularisation, inference, and the interpretation of results within both theoretical and applied contexts. The examination assesses all module learning outcomes. (MLO1, MLO2, MLO3, MLO4)
The [PP1.1][MP1.2]summative computer assessment provides students with the opportunity to apply advanced statistical and computational methods within an interactive analytical setting. Students will analyse datasets, construct and evaluate models, interpret outputs, and apply statistical reasoning to support conclusions and decision making within complex or high-dimensional contexts. The assessment particularly supports the evaluation of practical analytical and interpretative skills associated with advanced modelling approaches. (MLO3, MLO4)
The formative computer assessment provides opportunities for students to practise advanced statistical reasoning, modelling, and interpretation skills while receiving feedback in advance of the summative assessments.
The assessment structure supports the development of both conceptual understanding and applied analytical skills, enabling students to evaluate, interpret, and apply advanced statistical and learning approaches within complex real-world contexts.
Alternative assessment:
For students with a Student Support Plan (SSP) who have alternative assessment as a reasonable adjustment and/or students with supported Personal Extenuating Circumstances (PEC), it may be necessary to set an alternative assessment as presented below. Due to the professional requirements of some programmes, alternative assessments may not be available even where students are eligible.
An oral examination comprising a 15-minute prepared topic followed by 15 minutes of questions from the overall module syllabus may be offered as an alternative to the written examination where appropriate.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC3007's Timetable