This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Postgraduate Handbook 2019-2020 (archived)
Module PHYS51430: Core I: Statistics, Machine Learning, Scientific and High Performance Computing
Department: Physics
PHYS51430: Core I: Statistics, Machine Learning, Scientific and High Performance Computing
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Tied | 5 | 30 | Available in 2019/2020 | None. | Durham |
Tied To
Pre-requisites
Corequisites
Excluded Combinations
Aims
- Provide basic knowledge and critical understanding of paradigms, fundamental ideas and methods of data analysis and statistics
- Provide basic knowledge and critical understanding of paradigms, fundamental ideas and methods of machine learning
- Provide basic knowledge and critical understanding of paradigms, technologies and trends in High Performance Computing (HPC)
- Provide basic knowledge and critical understanding of paradigms, fundamental ideas, algorithms and methods of numerical simulation
Content
- Introduction to statistics and data analysis
- Introduction to Machine Learning, classification and regression
- Introduction to High-Performance Computing
- Introduction to numerical methods, scientific computing and simulation
Learning Outcomes
Subject-specific Knowledge
- understanding and critical reflection of fundamental ideas and techniques in the application of data analysis and statistics to scientific data
- understanding and critical reflection of fundamental ideas and techniques in the application of machine learning to scientific data
- understanding and critical reflection of paradigms and relevant techniques in high-performance computing
- understanding and critical reflection of ideas, numerical techniques and algorithms in scientific computing and simulation
Subject-specific Skills
- competent and educated selection and application of programming languages, algorithms and computing tools for specific problems
Key Skills
- familiarity with basic paradigms and modern concepts underlying scientific computing and data analysis
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures in Introduction to Statistics and Data Analysis | 8 | 2 per week | 60 minutes | 8 | |
| Practical Classes in Introduction to Statistics and Data Analysis | 8 | 2 per week | 60 minutes | 8 | |
| Lectures in Introduction to Machine Learning | 8 | 2 per week | 60 minutes | 8 | |
| Practical Classes in Introduction to Machine Learning | 8 | 2 per week | 60 minutes | 8 | |
| Lectures in Introduction to High-Performance Computing | 8 | 1 per week | 60 minutes | 8 | |
| Computer Labs in Introduction to High-Performance Computing | 8 | 3 per week | 60 minutes | 8 | |
| Lectures in Introduction to Scientific Computing | 8 | 2 per week | 60 minutes | 8 | |
| Computer Labs in Introduction to Scientific Computing | 8 | 2 per week | 60 minutes | 8 | |
| Self-study | 236 | ||||
| Total | 300 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Coursework | 100% | Scientific Computing Coursework with an emphasis on High-Performance Computing | 8 weeks | 50% | |
| Statistics and Machine Learning Coursework | 4 weeks | 25% | |||
| Data Analysis Coursework | 5 weeks | 25% |
Formative Assessment
Feedback on coursework.
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.