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Durham University

Postgraduate Modules 2019/2020

Module Description

Please ensure you check the module availability box for each module outline, as not all modules will run in each academic year. Each module description relates to the year indicated in the module availability box. Please be aware that modules may change from year to year, and may be amended to take account of, for example: changing staff expertise, disciplinary developments, the requirements of external bodies and partners, and student feedback.

Department: Physics

PHYS51430: Core I: Statistics, Machine Learning, Scientific and High Performance Computing

Type Tied Level 5 Credits 30 Availability Available in 2019/20

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 Contact 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


    If you have a question about Durham's modular degree programmes, please visit our User Guide. If you have a question about modular programmes that is not covered by the User Guide, or a query about the Postgraduate Module Handbook, please contact us using the Comments and Questions form below.