Postgraduate Programme and Module Handbook 2018-2019 (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 | Tied | Level | 5 | Credits | 30 | Availability | Not available in 2018/19 | Module Cap |
---|

#### Prerequisites

#### Corequisites

#### Excluded Combination of Modules

#### 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 | |
---|---|---|---|---|---|

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 |

#### Summative Assessment

Component: Coursework | Component Weighting: 100% | ||
---|---|---|---|

Element | Length / duration | Element Weighting | Resit Opportunity |

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.

■ 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