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Statistical Techniques for Data Analysis and Machine Learning - PHY00047M

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  • Department: Physics
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

This module aims to convey the understanding, experience, and application of statistical methods in physics necessary for unbiased evaluation of data (either experimental or theoretical). The module introduces advanced methods in data analysis, which includes areas of Maximum Likelihood, fitting methods, and confidence regions. The module will also cover topics on ML and neural networks, the Law of large numbers and its applications, random processes and Monte Carlo Techniques.

Module will run

Occurrence Teaching period
A Semester 2 2023-24

Module aims

This module aims to convey the understanding and experience in the use of statistical methods in physics necessary for unbiased evaluation of data (either experimental or theoretical). The module introduces advanced methods in data analysis, which includes areas of Maximum Likelihood, fitting methods, and confidence regions. It covers basic measures of data, probability, Bayesian analysis and elements of Bayesian statistics, probability distributions, errors including the central limit theorem, error propagation, estimators and the maximum likelihood estimator, expectation values of functions, fitting of data, decision trees, non-parametric bootstrap and hypothesis testing.

The module will also cover topics on ML and neural networks, the Law of large numbers and its applications, random processes and Monte Carlo Techniques, as well as provide the basics of object oriented programming that will be used in developing ML algorithms. Best approaches and ethics in data science will also be addressed.

Module learning outcomes

At the end of the module successful students will be able to:

  • demonstrate an understanding of the principles of underlying data analysis and extract information from data utilising various techniques, including χ and maximum likelihood.
  • utilise Monte Carlo Techniques in data analysis, as well as Machine learning and Neural Networks, as applied across data science.
  • compare and contrast methodologies in data analytics and assess, for concrete physical data at hand, which are the most effective and/or precise in order to undertake a robust analysis and presentation of the data.
  • demonstrate an understanding of the ethics involved in data science, and evaluate ethical constraints, as applicable.

Module content

The module will cover the following areas of statistical data analytics:

  • The basic statistics involved in the analysis of physical data.
  • The principles underlying data analysis.
  • The appropriate statistic for use in concrete fitting of data, including X ² and maximum likelihood methods.
  • Data fitting and evaluate the fit results, including error matrices, confidence limits and goodness-of-fit.
  • The use of maximum likelihood methods.
  • Confidence intervals or confidence regions in data analysis in general.
  • Monte Carlo Techniques in parameter estimation.
  • The usage and applications of Machine learning techniques.
  • The basic principles one needs to apply when presenting data and the ethics involved in data science.

The module will have the following assessments/reassessments:

Assessment

  • Essay: Continuous Assessment 20% (Throughout Semester)- problem based to evaluate the application of the LO. Students will be given datasets where they will need to apply statistical methods to extract information and critically reflect on the results.
  • Essay: Project 20% (End of Semester) - students work in pairs or groups to produce a Project report on a topic related to the Module.
  • Essay: Final Assignment 60% (due in CAP) - students work individually to analyse a provided data set using skills developed in the module. This is a short but in-depth investigation using appropriate statistical and ML techniques and critically evaluating the outputs.

Reassessment

  • Essay: Final Assignment 60% (due in the Re-Assessment period) - students work individually to analyse a provided data set using skills developed in the module. This is a short but in-depth investigation using appropriate statistical and ML techniques and critically evaluating the outputs.

Indicative assessment

Task % of module mark
Essay/coursework 20
Essay/coursework 60
Essay/coursework 20

Special assessment rules

Other

Indicative reassessment

Task % of module mark
Essay/coursework 60

Module feedback

'Feedback’ at a university level can be understood as any part of the learning process which is designed to guide your progress through your degree programme. We aim to help you reflect on your own learning and help you feel more clear about your progress through clarifying what is expected of you in both formative and summative assessments.

A comprehensive guide to feedback and to forms of feedback is available in the Guide to Assessment Standards, Marking and Feedback. This can be found at:

https://www.york.ac.uk/students/studying/assessment-and-examination/guide-to-assessment/

The School of Physics, Engineering & Technology aims to provide some form of feedback on all formative and summative assessments that are carried out during the degree programme. In general, feedback on any written work/assignments undertaken will be sufficient so as to indicate the nature of the changes needed in order to improve the work. Students are provided with their examination results within 25 working days of the end of any given examination period. The School will also endeavour to return all coursework feedback within 25 working days of the submission deadline. The School would normally expect to adhere to the times given, however, it is possible that exceptional circumstances may delay feedback. The School will endeavour to keep such delays to a minimum. Please note that any marks released are subject to ratification by the Board of Examiners and Senate. Meetings at the start/end of each semester provide you with an opportunity to discuss and reflect with your supervisor on your overall performance to date.

Our policy on how you receive feedback for formative and summative purposes is contained in our Physics at York Taught Student Handbook.

Indicative reading

To be confirmed.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.